Shashi Raj Pandey

Shashi Raj Pandey
Aalborg University · Connectivity Section | Department of Electronic Systems |

PhD in CSE
Network Economics | Game Theory | Wireless Networks | Distributed Machine Learning | Data Markets |

About

48
Publications
6,911
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456
Citations

Publications

Publications (48)
Preprint
Full-text available
Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to...
Preprint
Full-text available
This paper considers a market for Internet of Things (IoT) data that is used to train machine learning models. The data is supplied to the market platform through a network and the price of the data is controlled based on the value it brings to the machine learning model. We explore the correlation property of data in a game-theoretical setting to...
Preprint
Consider two data providers that want to contribute data to a certain learning model. Recent works have shown that the value of the data of one of the providers is dependent on the similarity with the data owned by the other provider. It would thus be beneficial if the two providers can calculate the similarity of their data, while keeping the actu...
Article
Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building l...
Preprint
Full-text available
In a practical setting towards better generalization abilities of client models for realizing robust personalized Federated Learning (FL) systems, efficient model aggregation methods have been considered as a critical research objective. It is a challenging issue due to the consequences of non-i.i.d. properties of client's data, often referred to a...
Preprint
Full-text available
In a Federated Learning (FL) setup, a number of devices contribute to the training of a common model. We present a method for selecting the devices that provide updates in order to achieve improved generalization, fast convergence, and better device-level performance. We formulate a min-max optimization problem and decompose it into a primal-dual s...
Preprint
Full-text available
As Machine Learning (ML) models are becoming increasingly complex, one of the central challenges is their deployment at scale, such that companies and organizations can create value through Artificial Intelligence (AI). An emerging paradigm in ML is a federated approach where the learning model is delivered to a group of heterogeneous agents partia...
Preprint
Full-text available
Internet of Things (IoT) connectivity has a prominent presence in the 5G wireless communication systems. As these systems are being deployed, there is a surge of research efforts and visions towards 6G wireless systems. In order to position the evolution of IoT within the 6G systems, this paper first takes a critical view on the way IoT connectivit...
Article
Full-text available
A recent take towards Federated Analytics (FA), which allows analytical insights of distributed datasets, reuses the Federated Learning (FL) infrastructure to evaluate the summary of model performances across the training devices. However, the current realization of FL adopts single server-multiple client architecture with limited scope for FA, whi...
Article
In this letter, a novel framework to deliver critical spread out URLLC services deploying unmanned aerial vehicles (UAVs) in an out-of-coverage area is developed. To this end, the resource optimization problem, i.e., resource blocks (RBs) and power allocation, and optimal UAV deployment strategy are studied for UAV-assisted 5G networks to jointly m...
Article
Full-text available
Unmanned aerial vehicles (UAVs) can provide an effective solution for improving the coverage, capacity, and the overall performance of terrestrial wireless cellular networks. In particular, UAV-assisted cellular networks can meet the stringent performance requirements of the fifth generation new radio (5G NR) applications. In this paper, the proble...
Article
Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we...
Article
In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slic...
Article
Full-text available
Due to the availability of huge amounts of data and processing abilities, current artificial intelligence (AI) systems are effective in solving complex tasks. However, despite the success of AI in different areas, the problem of designing AI systems that can truly mimic human cognitive capabilities such as artificial general intelligence, remains l...
Article
Full-text available
In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy and computation capability at the mobile devices, the performance of the model training is always a...
Preprint
In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy and computation capability at the mobile devices, the performance of the model training is always a...
Preprint
Full-text available
A recent take towards Federated Analytics (FA), which allows analytical insights of distributed datasets, reuses the Federated Learning (FL) infrastructure to evaluate the population-level summary of model performances. However, the current realization of FL adopts single server-multiple client architecture with limited scope for FA, which often re...
Preprint
Full-text available
In this letter, a novel framework to deliver critical spread out URLLC services deploying unmanned aerial vehicles (UAVs) in an out-of-coverage area is developed. To this end, the resource optimization problem, i.e., resource block (RB) and power allocation, are studied for UAV-assisted 5G networks to meet the objective of jointly maximizing the av...
Article
Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. Federated learning can be a promising solution for enabling IoT-based smart applications. In this article, we present the primary design aspects for enabling federated le...
Article
Full-text available
Unmanned aerial vehicles (UAVs) have been deployed to enhance the network capacity and provide services to mobile users with or without infrastructure coverage. At the same time, we have observed the exponential growth in Internet of Things (IoTs) devices and applications. However, as IoT devices have limited computation capacity and battery lifeti...
Preprint
Full-text available
Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) (Minh et al. 2020) lays out a holistic philosophy with underlying princ...
Preprint
Full-text available
Unmanned aerial vehicles (UAVs) can provide an effective solution for improving the coverage, capacity, and the overall performance of terrestrial wireless cellular networks. In particular, UAV-assisted cellular networks can meet the stringent performance requirements of the fifth generation new radio (5G NR) applications. In this paper, the proble...
Preprint
Recently, the coexistence of ultra-reliable and low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services on the same licensed spectrum has gained a lot of attention from both academia and industry. However, the coexistence of these services is not trivial due to the diverse multiple access protocols, contrasting frame distrib...
Preprint
Full-text available
Due to the availability of huge amounts of data and processing abilities, current artificial intelligence (AI) systems are effective at solving complex tasks. However, despite the success of AI in different areas, the problem of designing AI systems that can truly mimic human cognitive capabilities such as artificial general intelligence, remains l...
Preprint
Full-text available
In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slic...
Article
The interconnection of resource-constrained and globally accessible things with unreliable Internet make them vulnerable to attack such as, but not limited to, data forging, false data injection, and packet drop. Such attacks may affect mission-critical applications which rely on sensor data for decision-making processes, hence, necessitates high a...
Article
Full-text available
An efficient content caching policy at the edge of the mobile cellular network can improve the quality of services of the mobile users and reduces network congestion at the backhaul. On the other hand, the wireless network virtualization emerges as a cutting-edge technique to address the limited network capacity problem due to the exponential growt...
Conference Paper
Multimedia streaming for mobile users in the existing network architecture is facilitated by the enabling technologies such as content caching networks (CCNs) and proactive caching mechanisms at the network edge. However, the intermittent disruptions caused during media streaming due to the contention amongst increasing multimedia nodes to withhold...
Conference Paper
Full-text available
Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to the central aggregator for improving the global model. However, a key challenge is to maintain communication efficiency (i.e., the numb...
Article
Full-text available
Recently, the coexistence of ultra-reliable and low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services on the same licensed spectrum has gained a lot of attention from both academia and industry. However, the coexistence of these services is not trivial due to the diverse multiple access protocols, contrasting frame distrib...
Preprint
Full-text available
Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. Federated learning can be a promising solution for enabling IoT-based smart applications. In this paper, we present the primary design aspects for enabling federated lear...
Article
Full-text available
Long-Term Evolution in the Unlicensed Spectrum (LTE-U) is considered as an indispensable technology to mitigate the spectrum scarcity in wireless networks. Typical LTE transmissions are contention-free and centrally controlled by the Base Station (BS). However, the wireless networks that work in unlicensed bands use contention-based protocols for c...
Preprint
Full-text available
Wireless network slicing (i.e., network virtualization) is one of the potential technologies for addressing the issue of rapidly growing demand in mobile data services related to 5G cellular networks. It logically decouples the current cellular networks into two entities; infrastructure providers (InPs) and mobile virtual network operators (MVNOs)....
Preprint
Full-text available
Wireless network slicing (i.e., network virtualization) is one of the potential technologies for addressing the issue of rapidly growing demand in mobile data services related to 5G cellular networks. It logically decouples the current cellular networks into two entities; infrastructure providers (InPs) and mobile virtual network operators (MVNOs)....
Conference Paper
Full-text available
In this paper, we propose a dynamic resource scheduling of URLLC (Ultra-reliable low latency communication) and eMBB (Enhanced mobile broadband) services for the downlink in 5G networks. The eMBB traffic is characterized with the high bandwidth network services such as web browsing, video streaming, augmented reality, and the URLLC services require...
Article
Full-text available
LTE in the unlicensed band (LTE-U) is a promising solution to overcome the scarcity of the wireless spectrum. However, to reap the benefits of LTE-U, it is essential to maintain its effective coexistence with WiFi systems. Such a coexistence, hence, constitutes a major challenge for LTE-U deployment. In this paper, the problem of unlicensed spectru...
Preprint
Full-text available
LTE in the unlicensed band (LTE-U) is a promising solution to overcome the scarcity of the wireless spectrum. However, to reap the benefits of LTE-U, it is essential to maintain its effective coexistence with WiFi systems. Such a coexistence, hence, constitutes a major challenge for LTE-U deployment. In this paper, the problem of unlicensed spectru...
Preprint
Full-text available
In the Internet of Things (IoT), things can be connected to the Internet via IPv6 and 6LoWPAN networks. The interconnection of resource-constrained and globally accessible things with untrusted and unreliable Internet make things vulnerable to attacks including data forging, false data injection, packet drop and many more, resulting in an unreliabl...
Article
In mobile crowdsensing, the most significant challenge is to enable smart devices to perform various sensing tasks for diverse goal-oriented applications. This can be accomplished by the interaction of task owners with smart devices via a specific platform (application interface) to influence their acceptance for task completion, employing various...
Conference Paper
Mobile crowdsourcing paradigm is considered as one of the emerging techniques due to immense demand of location based services and various novel applications in recent years. The evolution of smart mobile users (SMUs), specifically due to high end mobile devices in terms of resources and capabilities has contributed towards the concept of collabora...
Conference Paper
Full-text available
Content-Centric-Networking (CCN) architecture brings contents near to the users through caching. If the requested contents location is unknown, the Content Router (CR) floods the user's requests to all of its neighbor routers for finding the content. This results in forwarding many unnecessary requests outside of the Autonomous System (AS), and als...

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