Xiuzhen Cheng

University of Science and Technology of China | USTC · School of Computer Science and Technology

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) by autonomous vehicle s (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability...

Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements insufficient for fault detection. To overcome these limitations, we propose Romeo as the fir...

Satellite-Ground Integrated Networks (SGINs) are promising network architectures that can help reduce the load on terrestrial networks, provide mega-access capabilities and intensive task offloading functions. However, traditional resource management methods are difficult to apply directly into SGINs due to their multi-layered, heterogeneous and dy...

Fueled by the rapid advances in high-speed mobile networks, live video streaming has seen explosive growth in recent years and some DASH-based algorithms were specifically proposed for low-latency video delivery. We conducted a measurement study for the state-of-the-art algorithms with large-scale network traces. It reveals that these algorithms ar...

Satellite-Ground Integrated Networks (SGINs) are promising network architectures that can help reduce the load on terrestrial networks, provide mega-access capabilities and intensive task offloading functions. However, traditional resource management methods are difficult to apply directly into SGINs due to their multi-layered, heterogeneous and dy...

Video surveillance systems play a crucial role in ensuring public safety and security by capturing and monitoring critical events in various areas. However, traditional surveillance cameras face limitations when it comes to malicious physical damage or obscuring by offenders. To overcome this limitation, we propose m $^{2}$ Vision , which is the...

We investigate over-the-air federated learning (OTA-FL) that exploits over-the-air computing (AirComp) to integrate communication and computation seamlessly for FL. Privacy presents a serious obstacle for OTA-FL, as it can be compromised by maliciously manipulating channel state information (CSI). Moreover, the limited band at edge hinders OTA-FL f...

In this paper, we propose De-RPOTA , a novel algorithm designed for decentralized learning, equipped with mechanisms for resource adaptation and privacy protection through over-the-air computation. We theoretically analyze the combined effects of limited resources and lossy communication on decentralized learning, showing it converges towards a c...

To efficiently train the billions of parameters in a giant model, sharing the parameter-fragments within the Federated Learning (FL) framework has become a popular pattern, where each client only trains and shares a fraction of parameters, extending the training of giant models to the broader resources-constrained scenarios. Compared with the previ...

Short video platforms have become important channels for news dissemination, offering a highly engaging and immediate way for users to access current events and share information. However, these platforms have also emerged as significant conduits for the rapid spread of misinformation, as fake news and rumors can leverage the visual appeal and wide...

Large Language Models (LLMs) excel in diverse tasks such as text generation, data analysis, and software development, making them indispensable across domains like education, business, and creative industries. However, the rapid proliferation of LLMs (with over 560 companies developing or deploying them as of 2024) has raised concerns about their o...

Integrated Non-Terrestrial and Terrestrial Networks (NTN-TN) have been emerging as a promising architecture to facilitate state sensing, computation migration, and privacy protection for terrestrial users, where both low, medium and geostationary orbit satellites collaboratively support various tasks and allocate resources effectively. Nevertheless...

Satellite-ground twin networks (SGTNs) are regarded as a promising service paradigm, which can provide mega access services and powerful computation offloading capabilities via cloud-fog automation functions. Specifically, cloud-fog automation technologies are collaboratively leveraged to enable dense connectivity, pervasive computing, and intellig...

Fault-tolerant wireless consensus systems face numerous challenges in practical applications, among which unstable communication poses a pivotal issue. Some prior works achieve consensus among all participating nodes by relying on a dependable wireless channel, e.g., (Yang et al., TUP Tsinghua Sci Technol 27(5):817–831, 2022) and (Xu et al., Consen...

Byzantine faults describe the abnormal behavior of certain nodes in wireless systems due to hardware failures, network issues, software errors, or malicious attacks, which may be difficult to predict or understand by other normal nodes. Therefore, Byzantine fault-tolerant (BFT) wireless consensus emerged. Byzantine fault-tolerant (BFT) consensus is...

The importance of wireless consensus in modern wireless communication systems is growing significantly. The proliferation of Internet of Things (IoT) devices and the shift toward edge computing have highlighted the need for effective consensus mechanisms in wireless networks. These networks often operate in decentralized environments where devices...

No single wireless consensus algorithm can meet the needs of all application scenarios. In different application contexts, selecting the appropriate wireless consensus algorithm can significantly enhance the efficiency of the entire system, whether in production environments or in daily life. The correct choice not only improves efficiency but also...

Wireless blockchain protocols are an innovative technology that applies blockchain technology to the wireless network domain. They can be categorized into two main approaches. The first approach involves directly deploying blockchain protocols into wireless networks. This method utilizes existing blockchain technologies, such as Bitcoin or Ethereum...

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) by autonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability fo...

NFC tag authentication is crucial for preventing tag misuse. Existing NFC fingerprinting methods use physical-layer signals, which incorporate tag hardware imperfections, for authentication purposes. However, these methods suffer from limitations such as low scalability for a large number of tags or incompatibility with various NFC protocols, hinde...

Betweenness centrality (BC), a classic measure which quantifies the importance of a vertex to act as a communication "bridge" between other vertices in the network, is widely used in many practical applications. With the advent of large heterogeneous information networks (HINs) which contain multiple types of vertices and edges like movie or biblio...

Backdoor attacks on deep neural networks have emerged as significant security threats, especially as DNNs are increasingly deployed in security-critical applications. However, most existing works assume that the attacker has access to the original training data. This limitation restricts the practicality of launching such attacks in real-world scen...

With the widespread adoption of blockchain technology, the transaction fee mechanism (TFM) in blockchain systems has become a prominent research topic. An ideal TFM should satisfy user incentive compatibility (UIC), miner incentive compatibility (MIC), and miner-user side contract proofness ($c$-SCP). However, state-of-the-art works either fail to...

The paper studies a fundamental federated learning (FL) problem involving multiple clients with heterogeneous constrained resources. Compared with the numerous training parameters, the computing and communication resources of clients are insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heteroge...

The high resource consumption of large-scale models discourages resource-constrained users from developing their customized transformers. To this end, this paper considers a federated framework named Fed-Grow for multiple participants to cooperatively scale a transformer from their pre-trained small models. Under the Fed-Grow, a Dual-LiGO (Dual Lin...

Hypergraphs are applicable to various domains such as social contagion, online groups, and protein structures due to their effective modeling of multivariate relationships. However, the increasing size of hypergraphs has led to high computation costs, necessitating efficient acceleration strategies. Existing approaches often require consideration o...

Digital watermarking methods are commonly used to safeguard digital media copyrights by confirming ownership and deterring unauthorized use. However, without reliable third-party oversight, these methods risk security vulnerabilities during watermark extraction. Furthermore, digital media lacks tangible ownership attributes, posing challenges for s...

The safety of decentralized reinforcement learning (RL) is a challenging problem since malicious agents can share their poisoned policies with benign agents. The paper investigates a cooperative backdoor attack in a decentralized reinforcement learning scenario. Differing from the existing methods that hide a whole backdoor attack behind their shar...

Artificial intelligence (AI) has revolutionized various facets of human society and conferred significant advantages to numerous domains, such as entertainment, e-commerce, social media, healthcare, finance, and defense. However, as AI systems are increasingly employed in critical and sensitive scenarios, such as medical diagnosis, financial fraud...

We study federated unlearning, a novel problem to eliminate the impact of specific clients or data points on the global model learned via federated learning (FL). This problem is driven by the right to be forgotten and the privacy challenges in FL. We introduce a new framework for exact federated unlearning that meets two essential criteria: commun...

With the rapid development of blockchain and its applications, the amount of data stored on decentralized storage networks (DSNs) has grown exponentially. DSNs bring together affordable storage resources from around the world to provide robust, decentralized storage services for tens of thousands of decentralized applications (dApps). However, exis...

Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and...

Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the performance of local models by integrating class prototypes. However, prototype-based federated learning faces several challenges, such as prototype redundancy and protot...

Federated edge learning (FEEL) is a novel paradigm that enables privacy-preserving and distributed machine learning on end devices. However, FEEL faces challenges from data/system heterogeneity among the participating clients and resource constraints of edge networks, which affect the efficiency and accuracy of the learning process. In this paper,...

Big data and strong computing power have promoted artificial intelligence to the era of big models. In particular, ChatGPT’s debut heralded the vigorous development of large models. It is an urgent problem to train large models with trillion-level parameters efficiently. Traditional single-machine training stores all data and model parameters in me...

Hypergraphs are instrumental in modeling complex relational systems that encompass a wide spectrum of high-order interactions among components. One prevalent analysis task is the properties estimation of large-scale hypergraphs, which involves selecting a subset of nodes and hyperedges while preserving the characteristics of the entire hypergraph....

Many emerging applications in edge computing require processing of huge volumes of data generated by end devices, using the freshest available information. In this paper, we address the distributed optimization of multi-user long-term average Age-of-Information (AoI) objectives in edge networks that use NOMA transmission. This poses a challenge of...

Sound eavesdropping using light has been an area of considerable interest and concern, as it can be achieved over long distances. However, previous work has often lacked stealth (e.g., active emission of laser beams) or been limited in the range of realistic applications (e.g., using direct light from a device's indicator LED or a hanging light bul...

The openness of wireless networks opens the door to Byzantine attacks on the physical channels, making the communications unreliable and resulting in more challenges in achieving consensus among mobile devices. To address this issue, this paper studies the Byzantine-fault-tolerant (BFT) consensus problem based on an unreliable Byzantine communicati...

Game theory is an effective analytical tool for crowdsourcing. Existing studies based on it share a commonality: the influence of players’ decisions is bilateral . However, the status is broken by the zero-determinant (ZD) strategy, where the ZD player can unilaterally control the opponent's expected payoff. Thereby, crowdsourcing games trigger...

The integration of directed acyclic graph (DAG)- based blockchain and Internet of Things (IoT) aims at improving the efficiency of data storage. However, if massive IoT data are not placed in an organized way, the search and usage of the data for upper-level applications can be burdensome, since they have to examine the data block by block, which a...

This article explores the critical challenges and limitations of modern communication-based train control (CBTC) systems, particularly focusing on the dynamic and uncertain nature of train–ground communication. The concept of the Age of Information (AoI) is introduced, highlighting the discrepancies between the actual and derived states used for co...

Neural network models have become integral to Internet of Things (IoT) systems, with applications spanning from industrial automation to critical infrastructure management. Despite their prevalence, the deployment of these models within IoT systems introduces distinctive security vulnerabilities. In particular, adversaries may execute model poisoni...

Fueled by Metaverse, 360° video streaming has seen tremendous growth in the past years. However, our measurement reveals that current 360° streaming systems suffer from a dilemma that severely limits QoE. On the one hand, viewport prediction requires the shortest possible prediction distance for high predicting accuracy; On the other hand, video tr...

Supply Chain Management (SCM), a critical factor in enhancing companies’ efficiency and competitiveness, has received significant attention from both industry and academia. Beyond the flow of products from supplier to customer, SCM also involves the flow of information necessary to monitor, track, and optimize the entire product lifecycle. Conseque...

Recent studies have demonstrated the feasibility of eavesdropping on audio via radio frequency signals or videos, which capture physical surface vibrations from surrounding objects. However, these methods are inadequate for intercepting internally transmitted audio through wired media. In this work, we introduce radio-frequency retroreflector attac...

Byzantine fault-tolerant (BFT) consensus is a critical problem in parallel and distributed computing systems, particularly with potential adversaries. Most prior work on BFT consensus assumes reliable message delivery and tolerates arbitrary failures of up to $\frac{n}{3}$ nodes out of n total nodes. However, many systems face unpredictable mes...

Internet of Things (IoT) devices are frequently deployed in highly dynamic environments and need to continuously learn new classes from data streams. Incremental Learning (IL) has gained popularity in IoT as it enables devices to learn new classes efficiently without retraining model entirely. IL involves fine-tuning the model using two sources of...

The natural forking severely compromises the security and wastes resources of Blockchain. Current analyses of the natural forking are carried out from microscale and macroscale perspectives, each facing challenges in generality and accuracy respectively. This results in dire straits that the forking arising from the network layer cannot be solv...

Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effect...

We consider a $K$ -armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The goal is to let each of the agents eventually learn the best arm. Although recent studies show the power of collaboration among the agents in improving the efficacy o...

Cross-chain technology facilitates the interoperability among isolated blockchains on which users can freely communicate and transfer values. Existing cross-chain protocols suffer from the scalability problem when processing on-chain transactions. Off-chain channel, as a promising blockchain scaling technique, can enable micro-payment transactions...

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. Firs...

Though voting-based consensus algorithms in blockchain outperform proof-based ones in energy-and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this...

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violation...

Due to the substantial computational cost of neural network training, adopting third-party models has become increasingly popular. However, recent works demonstrate that third-party models can be poisoned. Nonetheless, most model poisoning attacks require reference data, e.g., training dataset or data belonging to the target label, making them diff...

Large-scale graphs usually exhibit global sparsity with local cohesiveness, and mining the representative cohesive subgraphs is a fundamental problem in graph analysis. The $k$ -truss is one of the most commonly studied cohesive subgraphs, in which each edge is formed in at least $k-2$ triangles. A critical issue in mining a $k$ -truss lies i...

A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse...

Searching for $k$ -cliques in graphs has been an important problem in graph analysis due to its large number of applications. Previously, finding $k$ -cliques in weighted graphs aimed at finding cliques with the largest sum of weight (with no distinction between the edge or the vertex weights), usually called the sum model. However, the algorit...

Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algorithms may fail when dealing with real-world irregular data that behaves in a heavy-tailed manner. To address this issue, we study the stochastic convex and non-convex opti...

This paper studies batch processing of core maintenance in hypergraph streams. We focus on updating the coreness of each vertex after the hypergraph evolves. Unlike existing works that mainly focus on exact coreness updates for the single hyperedge dynamic or approximate update, we propose the first known batch processing algorithms for exact core...

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyperparameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission t...

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge computing environments while preservin...

Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and...

The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining. In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs, only very few indexes are proposed for the same in bipartite graphs. In this work, we present the index called α(β)-core number on vertices, which re...

Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algorithms may fail when dealing with real-world irregular data that behaves in a heavy-tailed manner. To address this issue, we study the stochastic convex and non-convex opti...

With the continuous improvement of information infrastructures, academia and industry have been constantly exploring new computing paradigms to fully exploit computing powers. In this paper, we propose Meta Computing, a new computing paradigm that aims to utilize all available computing resources hooked on the Internet, provide efficient, fault-tol...

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we prop...

With the continuous improvement of information infrastructures, academia and industry have been constantly exploring new computing paradigms to fully exploit computing powers. In this paper, we propose Meta Computing, a new computing paradigm that aims to utilize all available computing resources hooked on the Internet, provide efficient, fault-tol...

bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Overprivilege attack , a widely reported phenomenon in IoT that accesses unauthorized or excessive resources, is notoriously hard to prevent, trace and mitigate. In this paper, we propose TBAC, a Tokoin-Based Access Control model enabled by blockchain an...

With the rapid development of wireless communication technology and wide implementation of mobile devices, the distributed clouding has changed our lives. As one of the fundamental building blocks in distributed clouding, the connectivity problem on the user devices significantly impacts the quality of services provided by the distributed clouding....

Federated Learning (FL) offers collaborative machine learning without data exposure, but challenges arise in the mobile edge network (MEC) environment due to limited resources and dynamic conditions. This paper presents a Digital Twin (DT)-assisted FL platform for MEC networks and introduces a novel multi-FL service framework to address resource dy...

A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse...

As an amalgamation of digital twin and edge computing, the digital twin edge networks (DITENs) have drawn much attention from industry and academia to bridge the divide between physical edge networks and digital systems. Meanwhile, the physical hardware and open-access wireless communication environments in edge networks raise significant challenge...

Although proof of work (PoW) consensus dominates the current blockchain-based systems mostly, it has always been criticized for the uneconomic brute-force calculation. As alternatives, energy-conservation and energy-recycling mechanisms heaved in sight. In this paper, we propose proof of user similarity (PoUS), a distinct energy-recycling consensus...