No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things
Abstract
This letter presents a federated learning-based data-accumulation scheme that combines drones and blockchain for remote regions where Internet of Things devices face network scarcity and potential cyber threats. The scheme contains a two-phase authentication mechanism in which requests are first validated using a cuckoo filter, followed by a timestamp nonce. Secure accumulation is achieved by validating models using a Hampel filter and loss checks. To increase the privacy of the model, differential privacy is employed before sharing. Finally, the model is stored in the blockchain after consent is obtained from mining nodes. Experiments are performed in a proper environment, and the results confirm the feasibility of the proposed scheme.
... Blockchain-based secure schemes are reliable when dealing with sensitive data. An FL-based blockchain-embedded data accumulation scheme using drones for IoT (FBI) was proposed in [97], in which a two-step data authentication method was applied. The first validation mechanism used a cuckoo filter, and the subsequent mechanism used a Hampel filter to calculate the loss function. ...
... This scheme showed better energy savings than benchmarks, but higher performance could be achieved if a loner training period was provided. The work in [97] demonstrated secure data aggregation in a remote region using FL and blockchain. This model has made good advancements toward secure aerial data aggregation and may be further improved by incorporating multi-UAV scenarios and focused energy savings. ...
... The IDAS [96] scheme measured the energy efficiency of the UAV trajectory using an aggregation ratio. The FBI [97] scheme improves the security of aerial data aggregation with blockchain and UAV identification mechanisms. The efficacy of the method was demonstrated in terms of the data transmission rate. ...
In recent years, unmanned aerial vehicles (UAVs) have been used to extend the Internet of things (IoT) framework owing to their vast applications, monitoring and surveillance capability, ubiquity, and mobility. To support IoT requirements, UAVs must be capable of aggregating, processing, and transmitting data in real-time basis. As not only the number of IoT devices but also the amount of data to be collected is increased, data aggregation is of great importance. Recently, the UAV can also function as a mobile edge computing server in association with aerial data aggregation. This paper is the first to survey the various aspects and techniques of UAV-based aerial data aggregation for IoT networks. After addressing key design issues, we review the existing data aggregation techniques along with possible future direction. They are then compared with each other in terms of major operational features, performance characteristics, advantages, and limitations. Open issues and research challenges are also discussed with possible solution approaches.
... The integration of AI and the blockchain is a potential solution in which to address the abovementioned challenges. Future communication networks must incorporate adaptive security solutions based on AI and blockchain principles [76,99]. These advanced security measures will play a critical role in securing IoE networks from potential threats and ensuring the transmitted data integrity, security, as well as the process, across the communication network [8]. ...
... However, future network technologies will raise concerns about cost, security, and privacy [98,100]. The technology of 6G can address security issues by introducing AI, the blockchain, and federated learning (FL) [76,77,99]. Blockchain-incorporated FL prevents malicious activities that can persist in conventional blockchain-based applications. ...
... Federated learning (FL) is an attractive AI paradigm that ensures privacy while preserving data [116,117]. The energy data owners (EDOs) can collectively train the shared AI model, except in cases of divulging the energy-oriented data by an edge-cloud-assisted FL framework [99,117]. It facilitates efficient and fortified energy data communication for the respective smart grid users by integrating FL and the blockchain [99]. ...
A well-functioning smart grid is an essential part of an efficient and uninterrupted power supply for the key enablers of smart cities. To effectively manage the operations of a smart grid, there is an essential requirement for a seamless wireless communication system that provides high data rates, reliability, flexibility, massive connectivity, low latency, security, and adaptability to changing needs. A contemporary review of the utilization of emerging 6G wireless communication for the major applications of smart grids, especially in terms of massive connectivity and monitoring, secured communication for operation and resource management, and time-critical operations, are presented in this paper. This article starts with the key enablers of the smart city, along with the necessity of the smart grid for the key enablers of it. The fundamentals of the smart city, smart grid, and 6G wireless communication are also introduced in this paper. Moreover, the motivations to integrate 6G wireless communication with the smart grid system are expressed in this article as well. The relevant literature overview, along with the novelty of this paper, is depicted to bridge the gap of the current research works. We describe the novel technologies of 6G wireless communication to effectively perform the considered smart grid applications. Novel technologies of 6G wireless communication have significantly improved the key performance indicators compared to the prior generation of the wireless communication system. A significant part of this article is the contemporary survey of the considered major applications of a smart grid that is served by 6G. In addition, the anticipated challenges and interesting future research pathways are also discussed explicitly in this article. This article serves as a valuable resource for understanding the potential of 6G wireless communication in advancing smart grid applications and addressing emerging challenges.
... The subject has been well reviewed [30]- [34]. A large number of blockchain applications have been proposed, including using blockchain to help secure wireless sensor based systems [35]- [48]. Indeed, the blockchain properties can be used to mitigate various threats to the sensing data. ...
... With data immutability of the blockchain, critical data can be stored on the blockchain to facilitate the integrity and availability of the sensing data. More specifically, blockchain has been used to facilitate sensor (IoT) authentication [49]- [53], access control [52], [54], anonymity/privacy [48], [51], [55]- [57], accountability [58], and trust [59]. ...
... Authentication [49]- [53] Higher demand on blockchain throughput Access control [52], [54] Anonymity/privacy [48], [51], [ , which offers decentralized storage of files. Second, the earlier system lacked a concrete mechanism to ensure that the sensor enrollment phase is secure. ...
In this paper, we present the design, implementation, and evaluation of a secure sensing data processing and logging system. The system is inspired and enabled by blockchain. In this system, a public blockchain is used as immutable data store to store the most critical data needed to secure the system. Furthermore, several innovative blockchain-inspired mechanisms have been incorporated into the system to provide additional security for the system’s operations. The first priority in securing sensing data processing and logging is admission control,
i.e
., only legitimate sensing data are accepted for processing and logging. This is achieved via a sensor identification and authentication mechanism. The second priority is to ensure that the logged data remain intact overtime. This is achieved by storing a small amount of data condensed from the raw sensing data on a public blockchain. A Merkel-tree based mechanism is devised to link the raw sensing data stored off-chain to the condensed data placed on public blockchain. This mechanism passes the data immutability property of a public blockchain to the raw sensing data stored off-chain. Third, the raw sensing data stored off-chain are secured with a self-protection mechanism where the raw sensing data are grouped into chained blocks with a moderate amount of proof-of-work. This scheme prevents an adversary from making arbitrary changes to the logged data within a short period of time. Fourth, mechanisms are developed to facilitate the search of the condensed data placed on the public blockchain and the verification of the raw sensing data using the condensed data placed on the public blockchain. The system is implemented in Python except the graphical user interface, which is developed using C#. The functionality and feasibility of the system have been evaluated locally and with two public blockchain systems, one is the IOTA Shimmer test network, and the other is Ethereum.
... One of the primary challenges involves the complexity of coordinating a large number of distributed devices or organizations engaged in model training. To address these challenges, various studies have integrated FL with blockchain technology [5,[16][17][18][19]. The unique characteristics of blockchain networks have the potential to significantly improve issues related to FL [20][21][22][23]. ...
... Anik Islam et al. [16] proposed a novel approach for data accumulation in IoT using drones. The scheme combines FL, allowing devices to train models collaboratively without sharing raw data, with blockchain technology to ensure data integrity and security. ...
This paper explores integrating federated learning (FL) and blockchain tech- nology, two burgeoning fields in information technology. Despite their growing popularity, both domains face significant challenges. In federated learning, the primary concern is safeguarding the integrity of the general model against client- induced compromises. Blockchain technology grapples with the need for a green mining approach through an energy-efficient consensus protocol. Our study lever- ages the strengths of each platform to mitigate the weaknesses of the other. We introduce an innovative blockchain-based FL model that eliminates the need for a central aggregator. Utilizing a green mining consensus algorithm named Proof of Accuracy (PoA), we create a competitive environment among nodes, fostering the creation of superior models. This approach ensures data integrity and model validation through a community-based consensus, resulting in a fully distributed system. This system enhances FL’s security and scalability and addresses vulner- abilities like malicious aggregators and scalability issues. Through experimental evaluations on the MNIST dataset with 20 miners, on one hand, our method enhances model accuracy to nearly 99% only after 10 blocks which is a higher point compared to FL and central learning. On the other hand, replacing Proof of Work (PoW) with PoA reduces energy consumption by nearly 30%. More- over, blockchain attacks appeared to be inapplicable, or resolvable after 6 blocks like fork attacks. After all, the introduced incentivizing mechanism lets malicious nodes get nearly zero rewards and allocates main rewards to honest nodes which is coherent with their efforts to present a superior model.
... In some systems, the BC is deployed by edge/fog nodes only. In other systems, cloud servers also participate in the authentication BC [47][48][49][50][51][52][53]. • Group Authentication: A group of resource-constrained nodes apply a common process to authenticate each other [41,[54][55][56]. ...
... A blockchain-based Federated Learning-assisted data collection scheme, in which drones are deployed to aid resource-constrained devices, was proposed in [49]. The proposed system uses differential privacy (DP) to ensure privacy during data gathering. ...
Abstract
The proliferation of resource-constrained devices has become prevalent across various digital applications, including smart homes, smart healthcare, and smart transportation, among others. However, the integration of these devices brings many security issues. To address these concerns, Blockchain technology has been widely adopted due to its robust security characteristics, including immutability, cryptography, and distributed consensus. However, implementing blockchain within these networks is highly challenging due to the limited resources of the employed devices and the resource-intensive requirements of the blockchain. To overcome these challenges, a multitude of researchers have proposed lightweight blockchain solutions specifically designed for resource-constrained networks. In this paper, we present a taxonomy of lightweight blockchain solutions proposed in the literature. More precisely, we identify five areas within the “lightweight” concept, namely, blockchain architecture, device authentication, cryptography model, consensus algorithm, and storage method. We discuss the various methods employed in each “lightweight” category, highlighting existing gaps and identifying areas for improvement. Our review highlights the missing points in existing systems and paves the way to building a complete lightweight blockchain solution for networks of resource-constrained devices.
... For instance, blockchains are being applied in the Internet of Things (IoT) environment across different fields such as healthcare services, leveraging technologies like the Internet of Skills [11]. Other areas where blockchain is being employed include automated manufacturing processes [12], secure data aggregation [13], mixed reality content sharding [13], and COVID pandemic monitoring [15]. Furthermore, in [16], the authors proposed an approach that combines blockchain and smart contracts for service management in IoT. ...
... For instance, blockchains are being applied in the Internet of Things (IoT) environment across different fields such as healthcare services, leveraging technologies like the Internet of Skills [11]. Other areas where blockchain is being employed include automated manufacturing processes [12], secure data aggregation [13], mixed reality content sharding [13], and COVID pandemic monitoring [15]. Furthermore, in [16], the authors proposed an approach that combines blockchain and smart contracts for service management in IoT. ...
The Metaverse is rapidly evolving, bringing us closer to its imminent reality. However, the widespread adoption of this new automated technology poses significant research challenges in terms of authenticity, integrity, interoperability, and efficiency. These challenges originate from the core technologies underlying the Metaverse and are exacerbated by its complex nature. As a solution to these challenges, this paper presents a novel framework based on Non-Fungible Tokens (NFTs). The framework employs the Proof-of-Stake consensus algorithm, a blockchain-based technology, for data transaction, validation, and resource management. PoS efficiently consume energy and provide a streamlined validation approach instead of resource-intensive mining. This ability makes PoS an ideal candidate for Metaverse applications. By combining NFTs for user authentication and PoS for data integrity, enhanced transaction throughput, and improved scalability, the proposed blockchain mechanism demonstrates noteworthy advantages. Through security analysis, experimental and simulation results, it is established that the NFT-based approach coupled with the PoS algorithm is secure and efficient for Metaverse applications.
... Yang et al. [39] proposed a decentralized blockchain-based federated learning architecture to defend against malicious devices using a secure global aggregation algorithm, and deploying a practical Byzantine fault-tolerant consensus protocol among multiple edge servers to prevent model tampering from malicious servers. Islam et al. [40] proposed a federated learning scheme combining drones and blockchain, which achieves a secure accumulation through a two-stage authentication mechanism, and introduces a differential privacy protection mechanism to improve the privacy of the model. ...
... Cont.40 Function AggregationRule(C, DIST):foreach [[dist ij ]] pk v in DIST do 42 dist ij ← CKKS.Decrypt([[dist ij ]] pk , sk v ); ...
COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.
... Paper [24] presents an FL-based data accumulation scheme for remote areas with limited connectivity and potential cyber threats. The proposed technique employed drones and blockchain networks for data collection and storage. ...
The growing adoption of Internet of Things (IoT) devices has led to a rising concern about the security of these networks. This paper proposes a proactive intrusion recognition method, FL‐IDPP, ensuring privacy preservation for IoT networks using federated learning (FL). The proposed approach employs bidirectional recurrent neural network (RNN) models to detect anomalies and identify potential intrusions. The proposed approach ensures data privacy and efficiency in the network by storing data locally on the IoT devices and only sharing the learned model weights with the central server for FL. A high accuracy of the global machine learning (ML) model is attained by incorporating a voting ensemble process for combining updates from multiple sources. The experimental results strongly advocate for the effectiveness of the proposed approach in recognizing potential intrusions in IoT networks with enhanced accuracy and data privacy.
... The outcomes were promising, displaying not only high accuracy but also reduced convergence time and bandwidth overhead relative to prevalent techniques. Tackling network scarcity and cybersecurity challenges in remote regions with scarce IoT devices, a novel Federated Learning-based Data Accumulation Scheme has been proposed [190]. Utilizing drones and blockchain technology, this approach emphasized a dual-phase authentication procedure that bolstered data privacy and protection. ...
As data become increasingly abundant and diverse, their potential to fuel machine learning models is increasingly vast. However, traditional centralized learning approaches, which require aggregating data into a single location, face significant challenges. Privacy concerns, stringent data protection regulations like GDPR, and the high cost of data transmission hinder the feasibility of centralizing sensitive data from disparate sources such as hospitals, financial institutions, and personal devices. Federated Learning addresses these issues by enabling collaborative model training without requiring raw data to leave its origin. This decentralized approach ensures data privacy, reduces transmission costs, and allows organizations to harness the collective intelligence of distributed data while maintaining compliance with ethical and legal standards. This review delves into FL’s current applications and its potential to reshape IoT systems into more collaborative, privacy-centric, and flexible frameworks, aiming to enlighten and motivate those navigating the confluence of machine learning and IoT advancements.
... For instance, Bouachir et al. introduced FederatedGrids as an energy-sharing platform by employing a PoW-based Ethereum blockchain along with federated learning[31]. Islam et al. presented an FL method that leverages a PoW-based blockchain to aggregate data sourced from IoT devices[32]. Baucas et al. devised a Fog-IoT platform dedicated to predictive healthcare. ...
Various deep learning techniques, including blockchain-based approaches, have been explored to unlock the potential of edge data processing and resultant intelligence. However, existing studies often overlook the resource requirements of blockchain consensus processing in typical Internet of Things (IoT) edge network settings. This paper presents our FLCoin approach. Specifically, we propose a novel committee-based method for consensus processing in which committee members are elected via the FL process. Additionally, we employed a two-layer blockchain architecture for federated learning (FL) processing to facilitate the seamless integration of blockchain and FL techniques. Our analysis reveals that the communication overhead remains stable as the network size increases, ensuring the scalability of our blockchain-based FL system. To assess the performance of the proposed method, experiments were conducted using the MNIST dataset to train a standard five-layer CNN model. Our evaluation demonstrated the efficiency of FLCoin. With an increasing number of nodes participating in the model training, the consensus latency remained below 3 s, resulting in a low total training time. Notably, compared with a blockchain-based FL system utilizing PBFT as the consensus protocol, our approach achieved a 90% improvement in communication overhead and a 35% reduction in training time cost. Our approach ensures an efficient and scalable solution, enabling the integration of blockchain and FL into IoT edge networks. The proposed architecture provides a solid foundation for building intelligent IoT services.
... Equipped with advanced sensors and autonomous navigation, these vehicles provide notable benefits such as increased efficiency, improved safety, and cost savings [2]. UAVs can effectively survey large areas for agriculture, environmental monitoring, and infrastructure inspection [3], while UGVs streamline logistics and transportation in industrial environments. USVs and UUVs excel in maritime applications, offering realtime data for port security, underwater exploration, and environmental monitoring [4]. ...
Unmanned any vehicles (UxVs), including aerial (UAVs), ground (UGVs), surface (USVs), and underwater (UUVs) vehicles, are transforming smart ecosystems by enhancing infrastructure, industry, and urban environments. However, the diverse technologies involved in UxVs make standardization and efficient management in ultra-dense networks challenging. Zero Touch Network (ZTN) technology leverages artificial intelligence and software-defined networks to automate network management, addressing these complexities but also introducing vulnerabilities to cyber threats such as False Data Injection (FDI) attacks. This paper introduces a secure data consolidation scheme where UxV operation data is validated with a deep learning model before being integrated into the ZTN for seamless management. A simplified transformer-based FDI attack detection model is proposed, enhanced through data augmentation and information gain-based feature selection. An experimental environment is established to demonstrate the feasibility of the proposed scheme, and it is shown that the proposed scheme outperforms existing methods.
... On the other hand, security and safety aspects have become crucial verticals in IoT applications [112]. As technology continues to advance and generate more data from sensors and high-resolution video cameras, there is an increasing demand for a scalable, adaptable, and cost-effective solution to evaluate this content in the aspect of real-time response. ...
In terms of digital transformation, organizations today are aware of the critical role that data and information play in their expansion and development in light of the Internet of Things. To increase network performance and stability, many applications are moving from cloud computing to edge computing (EC). However, in order to satisfy customers, applications like intelligent transportation systems, smart grids, smart cities, and healthcare call for even more effective services. This survey addresses extensive research on two aspects: firstly, we present the advancements of two application domains namely maritime areas and aerial systems in terms of integration with EC architecture. Secondly, we cover the most recent technologies, artificial intelligence (AI) and blockchain, combined into the EC paradigm by discussing several experiments conducted in various fields to demonstrate the value of utilizing them in the edge computing architecture. We analyze the results of eleven experiments in each technology from 2015 to 2023.
... In the realm of FANETs, the integration of AI techniques plays a pivotal role in optimizing resource allocation and energy usage. AI empowers FANETs to achieve intelligent and dynamic management of limited resources, ensuring efficient operation and prolonged network sustainability [13] [14] [15]. ...
... Since federated learning trains data locally and does not upload private data, the privacy overhead of the client is effectively reduced. Therefore, federated learning is widely used in the Industrial Internet of Things [6][7][8][9][10], Blockchain [11][12][13][14][15], and Smart Healthcare [16][17][18]. ...
... It has been an emerging application by using UAVs as aerial access points for data collection where the terrestrial telecommunication infrastructure, such as mobile data services, is unavailable [1]- [3]. There are numerous advantages to using UAVs for IoT data collection in the aspects of cost-effectiveness, adaptation to the environment, ad hoc network access of UAVs, as well as the possibility of a line-of-sight (LoS) transmission link thanks to the high altitude of the UAVs [4]- [6]. ...
This paper investigates the scenario of the Internet of things (IoT) data collection via multiple unmanned aerial vehicles (UAVs), where a novel collaborative multi-agent trajectory planning and data collection (CMA-TD) algorithm is introduced for online obtaining the trajectories of the multiple UAVs without any prior knowledge of the sensor locations. We first provide two integer linear programs (ILPs) for the considered system by taking the coverage and the total power usage as the optimization targets. As a complement to the ILPs and to avoid intractable computation, the proposed CMA-TD algorithm can effectively solve the formulated problem via a deep reinforcement learning (DRL) process on a double deep Q-learning network (DDQN). Extensive simulations are conducted to verify the performance of the proposed CMA-TD algorithm and compare it with a couple of state-of-the-art counterparts in terms of the amount of served IoT nodes, energy consumption, and utilization rates.
... Data acquisition through drone from a remote place is always vulnerable. A federated learning-based data-accumulation strategy that mixes drones and blockchain is being developed by Anik et al. 22 Cuckoo filter and timestamp nonce provides two phase authentication and Hampel filter and loss checks models secure the data accumulations. After getting approval from the miner accumulated data can be stored in blockchain. ...
In today's smart applications, very frequently used data like electronic health records (EHR's) are highly sensitive and soft target to the unauthorized agencies. Data management, integrity, and security issues plague EHR systems. Insurance firms are prohibited by law and corporate privacy from sharing patient data, but because the data are not linked and synchronized among insurance providers, there has been a rise in healthcare fraud. A crucial component of an e‐health system is safe communication between patients, healthcare providers, and insurance companies. Building a system to securely manage and track insurance operations by combining data from all sources is required to combat health insurance fraud. Over time, blockchain grew in popularity, and the healthcare sector's interest in it led to the development of design concepts that addressed security issues. In this study, a blockchain‐based security architecture has been presented to protect the EHR and provide a secure method for patients, their caregivers, physicians, and insurance agents to access the clinical data of the patients. By using off‐chain storage and cryptographic key exchange through blockchain our approach also covers the scalability, integrity and access control issue that a healthcare system in general is facing.
A crucial component of an e‐health system is safe communication between patients, healthcare providers, and insurance companies. A blockchain‐based security architecture has been presented to protect the EHR and provide a secure method for patients, caregivers, physicians, and insurance agents to access the clinical data of the patients.
... Edge computing, on the other hand, can provide efficient and low-latency computation and storage resources that can reduce the communication and computation overhead of FL. Researchers can explore the integration of blockchain and edge computing with FL to design a secure, efficient, and reliable FL framework for air quality monitoring and forecasting in smart cities [141,[170][171][172][173]. ...
Systems for monitoring air quality are essential for reducing the negative consequences of air pollution, but creating real-time systems encounters several challenges. The accuracy and effectiveness of these systems can be greatly improved by integrating federated learning and multi-access edge computing (MEC) technology. This paper critically reviews the state-of-the-art methodologies for federated learning and MEC-enabled air quality monitoring systems. It discusses the immense benefits of federated learning, including privacy-preserving model training, and MEC, such as reduced latency and improved response times, for air quality monitoring applications. Additionally, it highlights the challenges and requirements for developing and implementing real-time air quality monitoring systems, such as data quality, security, and privacy, as well as the need for interpretable and explainable AI-powered models. By leveraging such advanced techniques and technologies, air monitoring systems can overcome various challenges and deliver accurate, reliable, and timely air quality predictions. Moreover, this article provides an in-depth analysis and assessment of the state-of-the-art techniques and emphasizes the need for further research to develop more practical and affordable AI-powered decentralized systems with improved performance and data quality and security while ensuring the ethical and responsible use of the data to support informed decision making and promote sustainability.
... In FL, models are trained and share model parameters with the accumulator . The objective is to minimize the loss as follows [14]. ...
Smart cities embrace unmanned autonomous vehicles (UxVs) for urban mobility and addressing challenges. UxVs include UAVs, UGVs, USVs, and UUVs, empowered by AI, particularly deep learning (DL), for autonomous missions. However, traditional DL has limitations in adapting to dynamic environments and raises data privacy concerns. Limited data availability and starting from scratch to adapt to a new environment during missions pose challenges. Additionally, cyber threats, particularly in terms of communication and data security, can jeopardize the missions performed by UxVs. This paper proposes a federated transfer learning scheme for UxVs, sharing prior knowledge and training with limited data while ensuring security through blockchain. Domain adaptation with maximum mean discrepancy enhances the DL model's performance in target domains. The proposed scheme's feasibility is demonstrated in an empirical environment, and it outperforms existing works.
... In addition, security in the network is achieved by implementing blockchain technology using Solidity software for providing smart contract for vehicles in the network. 16,17 The structure of manuscript shows Section 2 presents the literature survey, Section 3 presents the proposed system framework, Section 4 presents the vehicle parameter analysis, Section 5 presents vehicle clustering and resource allocation for data offloading, Section 6 presents vehicle data security using blockchain, Section 7 presents results and discussion, and Section 8 presents conclusion. ...
When individuals are accustomed to receiving information in automobiles, mobile data offloading is becoming more common. However, the effects of movement of the vehicle in correspondence to relative speed and direction between vehicles, have a significant impact on mobile data offloading. An innovative deep learning algorithm namely, intelligent deep neural network (IDNN) is proposed for vehicle data offloading and an optimal algorithm namely, quasi opposition based C‐hen swarm optimization (QOCSO) is proposed for efficient vehicle resource allocation. Initially, the vehicles in the vehicular networks are clustered with the help of the cosine similarity‐based K‐means algorithm for transmitting the data in an energy‐aware manner. Then cluster heads (CHs) are optimally selected for the generated clusters using Boltzmann selection probability‐based earth worm algorithm. The selected CHs are responsible for collecting the data from the cluster members and that is forwarded to the roadside unit. Then the suitable mobile edge servers are selected according to the IDNN algorithm that offloads the data from the CHs to the appropriate server. These received tasks of the vehicles could be stored as a blockchain for providing security to the vehicular network and finally, the resource allocation of the incoming tasks to the vehicles is performed using the QOCSO algorithm. Experiment findings reveal that both offloading and resource scheduling techniques outperform existing state‐of‐the‐art vehicular network algorithms.
... The framework encompasses various fundamental implementations of horizontal federated learning (HFL), which incorporate techniques such as differential privacy [27] and secure aggregation. For instance, BlockFL, a notable HFL system proposed in [35], utilizes a blockchain network to facilitate updates of local learning models on devices while an FL-based blockchain solution is presented in [36]. In another study [37], the authors introduced MOCHA, an FL technique that addresses security challenges in multi-task settings, enabling multiple sites to collaborate on tasks while preserving privacy and security. ...
Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent challenges of imbalanced and noisy data impacting scalability and resilience, our study introduces two innovative algorithms crafted for FL within a peer-to-peer framework. These algorithms aim to enhance performance, especially in decentralized and resource-limited settings. Furthermore, we propose a client-balancing Dirichlet sampling algorithm with probabilistic guarantees to mitigate oversampling issues, optimizing data distribution among clients to achieve more accurate and reliable model training. Within the specifics of our study, we employed 10, 20, and 40 Raspberry Pi devices as clients in a practical FL scenario, simulating real-world conditions. The well-known FedAvg algorithm was implemented, enabling multi-epoch client training before weight integration. Additionally, we examined the influence of real-world dataset noise, culminating in a performance analysis that underscores how our novel methods and research significantly advance robust and efficient FL techniques, thereby enhancing the overall effectiveness of decentralized machine learning applications, including edge intelligence and edge caching.
... Above all, FL is still affected by privacy inference [13,16,17] and Byzantine attacks [18,19]. And these security risks and threats faced by FL in IoT applications are also increasingly prominent [12,20,21]. It is essential to study privacy protection and Byzantine-robust FL methods in IoT scenarios. ...
Federated learning has been widely applied because it enables a large number of IoT devices to conduct collaborative training while maintaining private data localization. However, the security risks and threats faced by federated learning in IoT applications are becoming increasingly prominent. Except for direct data leakage, there is also a need to face threats that attackers interpret gradients and infer private information. This paper proposes a Privacy Robust Aggregation Based on Federated Learning (PBA), which can be applied to multiple server scenarios. PBA filters outliers by using the approximate Euclidean distance calculated from binary sequences and the 3σ criterion. Then, this paper provides correctness analysis and computational complexity analysis on the aggregation process of PBA. Moreover, the performance of PBA is evaluated concerning ensuring privacy and robustness in this paper. The results indicate that PBA can resist Byzantine attacks and a state-of-the-art privacy inference, which means that PBA can ensure privacy and robustness.
... A potential solution to overcome these limitations is federated learning, which is an innovative approach that enhances data privacy in deep learning by utilizing a distributed learning system and centralized aggregation [33]. Recent studies have supported the FL-based approach as a popular and efficient way to facilitate privacy preservation in UAV applications [34,35]. FL enables clients (users) to keep their private data and train the model locally in limited epochs before sending only the model parameters to the aggregation server (the UAV). ...
Radio maps, which can provide metrics for signal strength at any location in a geographic space, are useful for many applications of 6G technologies, including UAV-assisted communication, network planning, and resource allocation. However, current crowd-sourced reconstruction methods necessitate large amounts of privacy-sensitive user data and entail the training of all data with large models, especially in deep learning. This poses a threat to user privacy, reducing the willingness to provide data, and consuming significant server resources, rendering the reconstruction of radio maps on resource-constrained UAVs challenging. To address these limitations, a self-supervised federated learning model called RadioSRCNet is proposed. The model utilizes a super-resolution (SR)-based network and feedback training strategy to predict the pathloss for continuous positioning. In our proposition, users retain the original data locally for training, acting as clients, while the UAV functions as a server to aggregate non-sensitive data for radio map reconstruction in a federated learning (FL) manner. We have employed a feedback training strategy to accelerate convergence and alleviate training difficulty. In addition, we have introduced an arbitrary position prediction (APP) module to decrease resource consumption in clients. This innovative module struck a balance between spatial resolution and computational complexity. Our experimental results highlight the superiority of our proposed framework, as our model achieves higher accuracy while incurring less communication overheads in a computationally and storage-efficient manner as compared to other deep learning methods.
... PETs are a group of methods, procedures, and techniques used to extract value from data and simultaneously reduce the privacy and security risks for private information [13]. PETs are crucial, especially in some areas such as unmanned aerial vehicles (UAVs) [14] and the healthcare domain, where sensitive data are extensively collected and used. In healthcare, the gathered patient data allow researchers and healthcare professionals to distinguish diseases, assist drug development, and improve public health. ...
Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.
... Islam et al. presented an FL-based data accumulation scheme that combined drones and blockchains to attain secure accumulation and privacy of the model [8]. Zhang et al. designed a blockchain-based model migration approach to achieve secure model migration and speed up the training of the model while minimizing computation costs [9]. ...
Federated learning (FL) is a technique that involves multiple participants who update their local models with private data and aggregate these models using a central server. Unfortunately, central servers are prone to single-point failures during the aggregation process, which leads to data leakage and other problems. Although many studies have shown that a blockchain can solve the single-point failure of servers, blockchains cannot identify or mitigate the effect of backdoor attacks. Therefore, this paper proposes a blockchain-based FL framework for defense against backdoor attacks. The framework utilizes blockchains to record transactions in an immutable distributed ledger network and enables decentralized FL. Furthermore, by incorporating the reverse layer-wise relevance (RLR) aggregation strategy into the participant’s aggregation algorithm and adding gradient noise to limit the effectiveness of backdoor attacks, the accuracy of backdoor attacks is substantially reduced. Furthermore, we designed a new proof-of-stake mechanism that considers the historical stakes of participants and the accuracy for selecting the miners of the local model, thereby reducing the stake rewards of malicious participants and motivating them to upload honest model parameters. Our simulation results confirm that, for 10% of malicious participants, the success rate of backdoor injection is reduced by nearly 90% compared to Vanilla FL, and the stake income of malicious devices is the lowest.
... Islam et al. [21], on the other hand, utilizes drones as a method to ensure connection between devices running on the edge (pure mist computing) and utilizes differential privacy alongside federated blockchain learning to further enhance privacy. However, each entity in the system must register before being allowed to participate, which can cause issues if the system is required to grow and shrink dynamically. ...
Machine learning, particularly using neural networks, is now widely adopted in practice even with the IoT paradigm; however, training neural networks at the edge, on IoT devices, remains elusive, mainly due to computational requirements. Furthermore, effective training requires large quantities of data and privacy concerns restrict accessible data. Therefore, in this paper, we propose a method leveraging a blockchain and federated learning to train neural networks at the edge effectively bypassing these issues and providing additional benefits such as distributing training across multiple devices. Federated learning trains networks without storing any data and aggregates multiple networks, trained on unique data, forming a global network via a centralized server. By leveraging the decentralized nature of a blockchain, this centralized server is replaced by a P2P network, removing the need for a trusted centralized server and enabling the learning process to be distributed across participating devices. Our results show that networks trained in such a manner have negligible differences in accuracy compared to traditionally trained networks on IoT devices and are less prone to overfitting. We conclude that not only is this a viable alternative to traditional paradigms but is an improvement that contains a wealth of benefits in an ecosystem such as a hospital.
... The authors focus on the impact of transmission parameters such as power and the number of miners on energy consumption through modeling and simulation that offer valuable insights and potential research directions for future work in this field. The negative impact of the energy limitation problem of drones on the service time is also discussed in a data collection BlockFL scheme [28]. ...
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data heterogeneity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.
... When electronic transactions and networking methods first emerged, the financial industry's cyber problem was initially viewed as a business issue [8]. A number of surveys have highlighted the importance of developing solid IT security strategies [9]. The use of various security measures to safeguard sensitive data is the subject of one of the surveys about business operations. ...
Data sharing is proposed because the issue of data islands hinders advancement of artificial intelligence technology in the 5G era. Sharing high-quality data has a direct impact on how well machine-learning models work, but there will always be misuse and leakage of data. The field of financial technology, or FinTech, has received a lot of attention and is growing quickly. This field has seen the introduction of new terms as a result of its ongoing expansion. One example of such terminology is “FinTech”. This term is used to describe a variety of procedures utilized frequently in the financial technology industry. This study aims to create a cloud-based intrusion detection system based on IoT federated learning architecture as well as smart contract analysis. This study proposes a novel method for detecting intrusions using a cyber-threat federated graphical authentication system and cloud-based smart contracts in FinTech data. Users are required to create a route on a world map as their credentials under this scheme. We had 120 people participate in the evaluation, 60 of whom had a background in finance or FinTech. The simulation was then carried out in Python using a variety of FinTech cyber-attack datasets for accuracy, precision, recall, F-measure, AUC (Area under the ROC Curve), trust value, scalability, and integrity. The proposed technique attained accuracy of 95%, precision of 85%, RMSE of 59%, recall of 68%, F-measure of 83%, AUC of 79%, trust value of 65%, scalability of 91%, and integrity of 83%.
... Specifically, the authors have proposed solutions to determine optimal travel on the aspects of air quality and costs [22], smart irrigation solutions [23], and smart environmental management models [24]. -In the smart living sector, studies focus on solutions to improve the life quality of residents, including smart travel recommendations [25], crowd monitoring and management [26], smart healthcare [27], and crowdsourcing schemes based on emerging technologies consisting of Drones, AI, and Blockchain [28]. -In the smart governance sector, proposals focus on proposing transparent and effective urban governance solutions, including infrastructure management with the participation and making-decision of residents [29], complex event management mechanisms [30], and real-time electronic voting solutions [31]. ...
... Like other vending services, a data entry must be indexed and retrieved before exchanging it through the blockchains. In [49], authors propose an FL method that uses a secure methodology for data aggregation from IoT devices. The proposed method describes the data accumulation process performed via a drone and the resulting data saved in a blockchain. ...
Recently, innovations in the Internet-of-Medical-Things (IoMT), information and communication technologies, and Machine Learning (ML) have enabled smart healthcare. Pooling medical data into a centralised storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated Learning (FL) overcomes the prior problems with a centralised aggregator server and a shared global model. However, there are two technical challenges: FL members need to be motivated to contribute their time and effort, and the centralised FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralised fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasise three main research streams based on a systematic analysis of blockchain-empowered (i) IoMT, (ii) Electronic Health Records (EHR) and Electronic Medical Records (EMR) management, and (iii) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications.
... For example, hybrid applications, such as [34] and [35] manage risk analysis and notifying solutions at the server side, which are prone to data leakage and are still suffering from high communication costs and high volume of the message exchange. There are other CT platforms developed by integration of blockchain and specified IoT networks such as Internet of Drones (IoDs) [36] to provide different services in healthcare [37] and for pandemic control [38]. In particular, Islam et al. [38] proposed a blockchain-enabled solution, which is an integration of IoT, artificial intelligence (AI), and blockchain, leveraging IoDs to automate a supervision scheme to monitor the crises. ...
Recently, as a consequence of the coronavirus disease (COVID-19) pandemic, dependence on contact tracing (CT) models has significantly increased to prevent the spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors , there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. This article addresses this gap and proposes the trustworthy blockchain-enabled system for an indoor CT (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof-of-Work (dPoW) credit-based consensus algorithm coupled with randomized hash window (W-Hash) and dynamic Proof-of-Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node's behavior based on its underlying credit-based mechanism. For achieving a high local-ization performance, we capitalize on the availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data-driven localization model based on bluetooth low-energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by the implementation of a highly accurate CT model while improving the users' privacy and security. Index Terms-Blockchain, bluetooth low energy (BLE), contact tracing (CT), indoor localization.
... 21 In addition, the FL concept has also been used in miscellaneous fields like the Internet of Things and drone technology. 22,23 The summary table for the recent related state-of-the-art works about FL and computational pathology is given in Table 1. The main limitation of these recent related studies is the lack of analysis of FL performance under different data distribution scenarios. ...
Colorectal cancer is the fourth fatal disease in the world, and the massive burden on the pathologists related to the classification of precancerous and cancerous colorectal lesions can be decreased by deep learning (DL) methods. However, the data privacy of the patients is a big challenge for being able to train deep learning models using big medical data. Federated Learning is a rising star in this era by providing the ability to train deep learning models on different sites without sacrificing data privacy. In this study, the Big Transfer model, which is a new General Visual Representation Learning method and six other classical DL methods are converted to the federated version. The effect of the federated learning is measured on all these models on four different data settings extracted from the MHIST and Chaoyang datasets. The proposed models are tested for single learning, centralized learning, and federated learning. The best AUC values of federated learning on Chaoyang are obtained by the Big Transfer and VGG models at 90.77% and 90.76%, respectively, whereas the best AUC value on MHIST is obtained by the Big Transfer model at 89.72%. The overall obtained results of models on all data settings show that the contribution of Federated Learning with respect to single learning is 4.71% and 11.68% for the “uniform” and “label-biased” data settings of Chaoyang, respectively, and 6.89% for the “difficulty level-biased” data setting of MHIST. Thus, it is experimentally shown that federated learning can be applied to the field of computational pathology for new institutional collaborations.
... Furthermore, they discussed a case study for UAVs in a 6G network using BC-based FL as a future technology. Islam et al. (2022) proposed FL based blockchain embedded data accumulation scheme that combines drones and remote IoT devices that are prone to cyber threats and network scarcity. To further enhance the privacy of the proposed scheme, they employed DP before sharing model updates. ...
Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in real-life scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches’ future directions and existing drawbacks are discussed in detail.
The Internet of Things (IoT) is a new well‐structured emerging technology with communication of smart devices using the 5G technology, infrastructures of roads, vehicles, smart cities, traffic systems and user applications. The IoT applications facilitate providing prompt emergency responses, and improved quality of vehicles, and road services, with cost‐effective activities in the intelligent transportation systems. Federated Learning (FL) enhances privacy and security in intelligent transportation systems and the Internet of Vehicles (IoV), using advanced prediction methods. Integrating blockchain with IoT, particularly in FL for transportation systems and IoV, bolsters security and data integrity. This approach keeps data local while only sharing model updates, enhancing privacy. Blockchain's transparency aids in efficient IoT collaboration, crucial for accountability. Its consensus algorithms further ensure network integrity, validating transactions and updates across devices, protecting against attacks, and fostering a transparent, collaborative environment. This comprehensive review paper delves into the innovative integration of blockchain technology with federated learning and the dynamic domain of IoV. It extensively analyzes the primary concepts, methodologies, and challenges associated with the deployment of FL in IoVs. This review presents a novel categorization examining three main types of blockchain‐based FL approaches vertical, horizontal, and decentralized each tailored to specific IoV communication scenarios like Vehicle‐to‐Vehicle (V2V), Vehicle‐to‐Infrastructure (V2I), and Vehicle‐to‐Cloud (V2C). It highlights FL applications in cyber‐attack detection, data sharing, traffic prediction, and privacy, considering Quality of Service factors. Finally, some main challenges and new open issues are discussed and assessed for federated machine learning approaches in the IoV.
Open radio access network (ORAN) plays a critical role in modern communication process. The structure that individual devices connect each other via ORAN turns to be a part of smart city. Incorporating with the concept Internet of Things (IoT), cloud-edge-client architecture has been accepted to discuss artificial intelligence (AI) coordinating functions in ORAN. Considering the security for ORAN in critical infrastructure, federated learning (FL) is an effective way to protect the original data on individual devices. However, recent schemes failed to support enough security features. To tackle the problem, we present a new highly secure aggregation and dropout-resilient FL scheme called HSADR which incorporates consortium blockchain and differential privacy to maintain the security environment. Second, we prove that the aggregation process reaches the IND-CCA2 security level, which is the first scheme to complete this goal. Last, experiments show that HSADR withstands common test aspects.
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data diversity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.
Conventional safety measures are inconsistent with inexpensive technologies like the Internet of Things (IoT) due to their significant storage traces, which are prohibitive to their utilization. The blockchain (BC) framework maintains the five essential security primitives: genuineness, credibility, secrecy, accessibility, and non-renunciation. Most IoT gadgets have limited resources, so a traditional blockchain deployment is inappropriate. Traditional deployment of blockchain computing in the Internet of Things leads to significant power consumption, delay, and computational inefficiency. The proposed solution improves the blockchain's conception to serve IoT technologies better. This article proposes a blockchain-based intelligent city design for the IoT that keeps all encryption safety precautions in place. Adding blockchain to an IoT platform does not add much extra labour. After comparing all safety requirements to existing literature, it is clear that the proposed method achieves satisfactory safety effectiveness.
The rapid growth of Internet of Things (IoT) devices has generated vast amounts of data, leading to the emergence of federated learning as a novel distributed machine learning paradigm. Federated learning enables model training at the edge, leveraging the processing capacity of edge devices while preserving privacy and mitigating data transfer bottlenecks. However, the conventional centralized federated learning architecture suffers from a single point of failure and susceptibility to malicious attacks. In this study, we delve into an alternative approach called decentralized federated learning (DFL) conducted over a wireless mesh network as the communication backbone. We perform a comprehensive network performance analysis using stochastic geometry theory and physical interference models, offering fresh insights into the convergence analysis of DFL. Additionally, we conduct system simulations to assess the proposed decentralized architecture under various network parameters and different aggregator methods such as FedAvg, Krum and Median methods. Our model is trained on the widely recognized EMNIST dataset for benchmarking handwritten digit classification. To minimize the model’s size at the edge and reduce communication overhead, we employ a cutting-edge compression technique based on genetic algorithms. Our simulation results reveal that the compressed decentralized architecture achieves performance comparable to the baseline centralized architecture and traditional DFL in terms of accuracy and average loss for our classification task. Moreover, it significantly reduces the size of shared models over the wireless channel by compressing participants’ local model sizes to nearly half of their original size compared to the baselines, effectively reducing complexity and communication overhead.
A key issue in current federated learning research is how to improve the performance of federated learning algorithms by reducing communication overhead and computing costs while ensuring data privacy. This paper proposed an efficient wireless transmission scheme termed the subsampling privacy-enabled RDP wireless transmission system (SS-RDP-WTS), which can reduce the communication and computing overhead in the process of learning but also enhance the privacy protection ability of federated learning. We proved our scheme's convergence and analyzed its privacy guarantee, as well as demoonstrated the performance of our scheme on the Modified National Institute of Standards and Technology database (MNIST) and Canadian Institute for Advanced Research, 10 classes datasets (CIFAR10).
In vehicular ad-hoc networks (VANET), federated learning enables vehicles to collaboratively train a global model for intelligent transportation without sharing their local data. However, due to dynamic network structure and unreliable wireless communication of VANET, various potential risks (e.g., identity privacy leakage, data privacy inference, model integrity compromise, and data manipulation) undermine the trustworthiness of intermediate model parameters necessary for building the global model. While existing cryptography techniques and differential privacy provide provable security paradigms, the practicality of secure federated learning in VANET is hindered in terms of training efficiency and model performance. Therefore, developing a secure and efficient federated learning in VANET remains a challenge. In this work, we propose a privacy-enhanced and efficient authentication protocol for federated learning in VANET, called FedComm. Unlike existing solutions, FedComm addresses the above challenge through user anonymity. First, FedComm enables vehicles to participate in training with unlinkable pseudonyms, ensuring both privacy preservation and efficient collaboration. Second, FedComm incorporates an efficient authentication protocol to guarantee the authenticity and integrity of model parameters originated from anonymous vehicles. Finally, FedComm accurately identifies and completely eliminates malicious vehicles in anonymous communication. Security analysis and verification with ProVerif demonstrate that FedComm enhances privacy and reliability of intermediate model parameters. Experimental results show that FedComm reduces the overhead of proof generation and verification by 67.38% and 67.39%, respectively, compared with the state-of-the-art authentication protocols used in federated learning.
Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient’s personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately.
The insufficient trustworthiness of fog nodes in fog computing leads to new security and privacy problems in communication between entities. Existing authentication schemes rely on a trusted third party, or assume that fog nodes are trustworthy, or the authentication overhead is high, which is inconsistent with the characteristics of fog computing. To solve the problem of secure communication in the fog computing environment, we propose an efficient blockchain-based secure remote authentication protocol for the fog-enabled Internet of Things (BSRA). Specifically, blockchain is introduced to construct distributed trust for the fog computing environment. Only lightweight cryptographic primitives such as physical unclonable functions (PUF) and cryptographic hash functions are exploited to design the authentication scheme. In addition, we use temporary identities and the authentication-piggybacking-synchronization to ensure the anonymity and effectiveness of the authentication scheme. We conduct security analysis to demonstrate that BSRA can provide guarantees against various known attacks. We also evaluate the performance of BSRA from several aspects, and the results show that BSRA is effective.
In this article, we present a random access (RA) scheme for federated learning (FL) over massive multiple-input–multiple-output (MIMO) systems to tackle the issue of some local devices not being able to compute their local models. This scheme adopts a multichannel model and allows devices to randomly select their uploading channels, and then the base station (BS) aggregates the local models received from channels directly based on the over-the-air computation. We call this scheme as RA-based FL over massive MIMO (RAFL-MIMO). Furthermore, to enable more devices to be involved in the FL process, we propose to utilize an access class barring (ACB) method to select the uploading devices and formulate an optimization problem of the ACB factor. We also derive the expected asymptotic convergence rate of the proposed RAFL-MIMO scheme to analytically show that the proposed RAFL-MIMO scheme can improve the performance of FL. Simulation results based on
-norm linear regression, and MNIST handwritten digits identification, Cifar-10 photograph classification show that the proposed RAFL-MIMO scheme significantly outperforms the case of the RAFL-MIMO without the ACB factor.
Information technology is an important direction for the development of traditional industries. With the in‐depth study of the industrial internet and blockchain technology in recent years, data‐sharing based on blockchain has become an important means to achieve industrial internet interconnection and has played an important role in achieving production efficiency and product quality. However, the transparency of blockchain can raise privacy issues, and competitors may engage in malicious competition due to the exposure of blockchain transactions. Therefore, based on the general blockchain system, this article unifies the scale of value of data sharing by introducing a token exchange mechanism as a basis for privacy‐preserving mechanisms. Based on smart contracts, a token mixer anonymizes shared participants. By introducing a ring signature scheme with smart contracts, we provide group‐oriented authentication for anonymous identities participating in data sharing and ensure data flow within a controlled range. The abovementioned mechanism provides a feasible solution for the privacy‐preserving requirement of data sharing among industrial entities and could encourage industrial entities to participate in data sharing, supporting the goals of interconnection in the industrial internet.
Unmanned aerial vehicles, drones, and internet of things (IoT) based devices have acquired significant traction due to their enhanced usefulness. The primary use is aerial surveying of restricted or inaccessible locations. Based on the aforementioned aspects, the current study provides a method based on blockchain technology for ensuring the safety and confidentiality of data collected by virtual circuit-based devices. To test the efficacy of the suggested technique, an IoT-based application is integrated with a simulated vehicle monitoring system. Pentatope-based elliptic curve encryption and secure hash algorithm (SHA) are employed to provide anonymity in data storage. The cloud platform stores technical information, authentication, integrity, and vehicular responses. Additionally, the Ethbalance MetaMask wallet is used for BCN-based transactions. Conspicuously, the suggested technique aids in the prevention of several attacks, including plaintext attacks and ciphertext attacks, on sensitive information. When compared to the state-of-the-art techniques, the outcomes demonstrate the effectiveness and safety of the suggested method in terms of operational cost (2.95 units), scalability (14.98 units), reliability (96.07%), and stability (0.82).
Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.
The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be not rich enough for seizing the ever-growing complexity and heterogeneity of the modern systems in the field. Traditional machine learning solutions assume the existence of (cloud-based) central entities that are in charge of processing the data. Nonetheless, the difficulty of accessing private data, together with the high cost of transmitting raw data to the central entity gave rise to a decentralized machine learning approach called
Federated Learning
. The main idea of federated learning is to perform an on-device collaborative training of a single machine learning model without having to share the raw training data with any third-party entity. Although few survey articles on federated learning already exist in the literature, the motivation of this survey stems from three essential observations. The first one is the lack of a fine-grained multi-level classification of the federated learning literature, where the existing surveys base their classification on only one criterion or aspect. The second observation is that the existing surveys focus only on some common challenges, but disregard other essential aspects such as reliable client selection, resource management and training service pricing. The third observation is the lack of explicit and straightforward directives for researchers to help them design future federated learning solutions that overcome the state-of-the-art research gaps. To address these points, we first provide a comprehensive tutorial on federated learning and its associated concepts, technologies and learning approaches. We then survey and highlight the applications and future directions of federated learning in the domain of communication and networking. Thereafter, we design a three-level classification scheme that first categorizes the federated learning literature based on the high-level challenge that they tackle. Then, we classify each high-level challenge into a set of specific low-level challenges to foster a better understanding of the topic. Finally, we provide, within each low-level challenge, a fine-grained classification based on the technique used to address this particular challenge. For each category of high-level challenges, we provide a set of desirable criteria and future research directions that are aimed to help the research community design innovative and efficient future solutions. To the best of our knowledge, our survey is the most comprehensive in terms of challenges and techniques it covers and the most fine-grained in terms of the multi-level classification scheme it presents.
Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging a reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers’ data. Then, manufacturers can predict customers’ requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers’ activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers’ privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.
By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables on-device machine learning without any centralized training data or coordination by utilizing a consensus mechanism in blockchain. Moreover, we analyze an end-to-end latency model of BlockFL and characterize the optimal block generation rate by considering communication, computation, and consensus delays.
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
Trusted third parties (TTPs) are frequently used for serving as an authority to issue and verify transactions in applications. Although the TTP-based paradigm provides customers with convenience, it causes a whole set of inevitable problems such as security threats, privacy vulnerabilities, and censorship. The TTP-based paradigm is not suitable for all modern networks, e.g., 5G and beyond networks, which are been evolving to support ubiquitous, decentralized, and autonomous services. Driven by the vision of blockchain technologies, there has been a paradigm shift in applications, from TTP-based to decentralized-trust-based. Decentralized applications (DApps) with blockchains promise no trust on authorities, tackling the key challenges of security and privacy problems. A main thrust of blockchain research is to explore frameworks and paradigms for decentralizing applications, fostering a number of new designs ranging from network architectures to business models. Therefore, this paper provides a compact and concise survey on the state-of-the-art research of decentralizing applications with blockchain in the 5G and beyond perspective. We provide four burning 5G and beyond challenges and discuss five aspects of motivation for decentralizing applications with blockchain. Then, we define nine fundamental modules of blockchains and explain the potential influence of these modules on decentralization in depth. We also discuss the interrelation between decentralization and some desired blockchain properties. Particularly, we present the capabilities of blockchain for decentralizing applications through reviewing DApps for 5G and beyond. We clearly distinguish three blockchain paradigms and discuss how developers to make right choices for 5G and beyond. Finally, we highlight important learned lessons and open issues in applying blockchain for decentralizing applications. Lessons learned and open issues from this survey will facilitate the transformation of centralized applications to DApps.
Edge-of-Things (EoT) enables the seamless transfer of services, storage and data processing from the Cloud layer to Edge devices in a large-scale distributed Internet of Things (IoT) ecosystems (e.g., Industrial systems). This transition raises the privacy and security concerns in the EoT paradigm distributed at different layers. Intrusion detection systems are implemented in EoT ecosystems to protect the underlying resources from attackers. However, the current intrusion detection systems are not intelligent enough to control the false alarms, which significantly lower the reliability and add to the analysis burden on the intrusion detection systems. In this article, we present a DaaS, Dew Computing as a Service for intelligent intrusion detection in EoT ecosystems. In DaaS, a deep learning-based classifier is used to design an intelligent alarm filtration mechanism. In this mechanism, the filtration accuracy is improved (or sustained) by using deep belief networks. In the past, the cloud-based techniques have been applied for offloading the EoT tasks, which increases the middle layer burden and raises the communication delay. Here, we introduce the dew computing features which are used to design the smart false alarm reduction system. DaaS, when experimented in a simulated environment, reflects lower response time to process the data in the EoT ecosystem. The revamped DBN model achieved the classification accuracy up to 95%. Moreover, it depicts a 60% improvement in the latency and 35% workload reduction of the cloud servers as compared to Edge intrusion detection system.
Unmanned aerial vehicles (UAVs) combined with artificial intelligence (AI) have opened a revolutionized way for mobile crowdsensing (MCS). Conventional AI models, built on aggregation of UAVs’ sensing data (typically contain private and sensitive user information), may arise severe privacy and data misuse concerns. Federated learning, as a promising distributed AI paradigm, has opened up possibilities for UAVs to collaboratively train a shared global model without revealing their local sensing data. However, there still exist potential security and privacy threats for UAV-assisted crowdsensing with federated learning due to vulnerability of central curator, unreliable contribution recording, and low-quality shared local models. In this paper, we propose SFAC, a
s
ecure
f
ederated learning framework for U
A
V-assisted M
C
S. Specifically, we first introduce a blockchain-based collaborative learning architecture for UAVs to securely exchange local model updates and verify contributions without the central curator. Then, by applying local differential privacy, we design a privacy-preserving algorithm to protect UAVs’ privacy of updated local models with desirable learning accuracy. Furthermore, a two-tier reinforcement learning-based incentive mechanism is exploited to promote UAVs’ high-quality model sharing when explicit knowledge of network parameters are not available in practice. Extensive simulations are conducted, and the results demonstrate that the proposed SFAC can effectively improve utilities for UAVs, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes.
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
In many networking systems, Bloom filters are used for high-speed set membership tests. They permit a small fraction of false positive answers with very good space efficiency. However, they do not permit deletion of items from the set, and previous attempts to extend "standard" Bloom filters to support deletion all degrade either space or performance.
We propose a new data structure called the cuckoo filter that can replace Bloom filters for approximate set membership tests. Cuckoo filters support adding and removing items dynamically while achieving even higher performance than Bloom filters. For applications that store many items and target moderately low false positive rates, cuckoo filters have lower space overhead than space-optimized Bloom filters. Our experimental results also show that cuckoo filters outperform previous data structures that extend Bloom filters to support deletions substantially in both time and space.
We introduce Adam, an algorithm for first-order gradient-based optimization
of stochastic objective functions. The method is straightforward to implement
and is based an adaptive estimates of lower-order moments of the gradients. The
method is computationally efficient, has little memory requirements and is well
suited for problems that are large in terms of data and/or parameters. The
method is also ap- propriate for non-stationary objectives and problems with
very noisy and/or sparse gradients. The method exhibits invariance to diagonal
rescaling of the gradients by adapting to the geometry of the objective
function. The hyper-parameters have intuitive interpretations and typically
require little tuning. Some connections to related algorithms, on which Adam
was inspired, are discussed. We also analyze the theoretical convergence
properties of the algorithm and provide a regret bound on the convergence rate
that is comparable to the best known results under the online convex
optimization framework. We demonstrate that Adam works well in practice when
experimentally compared to other stochastic optimization methods.
One approach to identifying outliers is to assume that the outliers have a different distribution from the remaining observations. In this article we define outliers in terms of their position relative to the model for the good observations. The outlier identification problem is then the problem of identifying those observations that lie in a so-called outlier region. Methods based on robust statistics and outward testing are shown to have the highest possible breakdown points in a sense derived from Donoho and Huber. But a more detailed analysis shows that methods based on robust statistics perform better with respect to worst-case behavior. A concrete outlier identifier based on a suggestion of Hampel is given.
Communication-efficient learning of deep networks from decentralized data
- B Mcmahan
- E Moore
- D Ramage
- S Hampson
- B A Y Arcas
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas,
"Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference
on Artificial Intelligence and Statistics, ser. Proceedings of Machine
Learning Research, A. Singh and J. Zhu, Eds., vol. 54. PMLR, 20-22
Apr 2017, pp. 1273-1282.
Adaptive federated optimization
- S J Reddi
- Z Charles
- M Zaheer
- Z Garrett
- K Rush
- J Konečný
- S Kumar
- H B Mcmahan
S. J. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný,
S. Kumar, and H. B. McMahan, "Adaptive federated optimization," in
9th International Conference on Learning Representations, ICLR 2021,
Virtual Event, Austria, May 3-7, 2021, 2021.
Adaptive federated optimization
- S J Reddi
Communication-efficient learning of deep networks from decentralized data
- mcmahan