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Article
Computer Science, Information Systems
Dinh C. Nguyen et al.
Summary: This article proposes a novel cooperative task offloading and block mining scheme for blockchain-based MEC system, aiming to maximize system utility by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. Simulation results demonstrate significant improvement of system utility compared to baseline approaches.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Qin Hu et al.
Summary: Mobile crowdsensing (MCS) utilizes the mobility of workers to help requestors accomplish sensing tasks more flexibly and at a lower cost. However, the large consumption of communication resources and high requirements on storage and computing capability hinder requestors with limited resources from using MCS. To address these challenges and promote the widespread application of MCS, we propose a novel MCS learning framework based on blockchain technology and federated learning, involving requestors, blockchain, edge servers, and mobile devices as workers.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Ferheen Ayaz et al.
Summary: This paper proposes a blockchain-assisted message dissemination solution based on federated learning to improve information dissemination efficiency, reduce time delay, and protect privacy. The proposed solution achieves high scalability in vehicular networks through theoretical and practical analysis.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Hardware & Architecture
Seyyedali Hosseinalipour et al.
Summary: This paper proposes a multi-stage hybrid federated learning (MH-FL) method, extending the traditional federated learning topology through the network dimension and considering a multi-layer cluster-based structure. The research results demonstrate the advantages of MH-FL in terms of resource utilization metrics.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2022)
Article
Computer Science, Information Systems
Chenyu Huang et al.
Summary: This research focuses on the limitations of reputation/trust-based blockchain systems and proposes a privacy-preserving scheme called zkRep. By periodically changing the identity and reputation of validators, zkRep successfully prevents slowly adaptive attacks. Experimental results demonstrate that zkRep provides great privacy protection with minimal overhead.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Dinh C. Nguyen et al.
Summary: In this paper, the authors investigate the issue of latency optimization in blockchain-based federated learning in multi-server edge computing. They propose an offloading strategy to assist ML model training for resource-constrained mobile devices and develop a decentralized ML model aggregation solution based on blockchain communications. The authors also formulate the problem as an optimization task and propose a deep reinforcement learning scheme to solve it. Numerical evaluation shows that their proposed scheme outperforms baselines in terms of model training efficiency, convergence rate, system latency, and robustness against attacks.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Viraaji Mothukuri et al.
Summary: FL enables collaborative training of ML models while preserving user data privacy. Ensuring secure trading/sharing of training data in the presence of adversarial FL clients is challenging. Blockchain-in-the-loop FL approach intertwines classic FL and Hyperledger Fabric, providing a secure framework for FL tasks.
IEEE SYSTEMS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Wenqi Shi et al.
Summary: In this paper, a joint device scheduling and resource allocation policy is proposed to maximize model accuracy within a given total training time budget for latency constrained wireless FL. The accuracy maximization problem is decomposed into two sub-problems and solved accordingly. Experimental results demonstrate the superiority of the proposed policy under various settings.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Frank Po-Chen Lin et al.
Summary: The paper introduces two timescale hybrid federated learning (TT-HF) architecture that combines device-to-server communication with device-to-device communication for model training. By studying the convergence behavior of TT-HF based on gradient diversity, new convergence bounds for distributed ML are established.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Qianpiao Ma et al.
Summary: Federated learning (FL) presents challenges of edge heterogeneity, Non-IID data, and communication resource constraints when training machine learning models over distributed edge nodes. In this paper, a semi-asynchronous federated learning mechanism (FedSA) is proposed to address these challenges more effectively by aggregating local models by arrival order and determining the number of participating workers to minimize training completion time. FedSA also deploys adaptive learning rates based on workers' participation frequency to improve training accuracy on Non-IID data, and extends the mechanism to dynamic and multiple learning tasks scenarios. Experimental results demonstrate the effectiveness of the proposed mechanism and algorithms in addressing these challenges.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Helin Yang et al.
Summary: The paper presents an asynchronous federated learning framework for multi-UAV-enabled networks, which allows for distributed computing and enhances federated convergence speed and accuracy through device selection strategy and A3C-based algorithm.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Yang Zhao et al.
Summary: This study introduces a federated learning system leveraging a reputation mechanism to assist home appliance manufacturers in developing a smart home system. The system involves two stages: centralized training of customer data and model sharing among manufacturers through a reputation-based mechanism.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Zhaohui Yang et al.
Summary: This paper investigates the problem of energy-efficient transmission and computation resource allocation for federated learning over wireless communication networks. An iterative algorithm is proposed to minimize energy consumption and numerical results show a reduction of up to 59.5% compared to conventional methods.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Yunlong Lu et al.
Summary: This article introduces digital twin edge networks (DITENs) to bridge the gap between physical edge networks and digital systems, and proposes a blockchain-empowered federated learning scheme to enhance communication security and data privacy protection. Additionally, asynchronous aggregation and digital twin empowered reinforcement learning are used to improve the efficiency of the integrated scheme.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Shuangwu Chen et al.
Summary: The study introduces a cooperative edge caching algorithm that alleviates the storage pressure of a single edge cache by enabling content sharing between base stations, effectively optimizing the utilization of mobile data traffic.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Mohammad Akbari et al.
Summary: The paper introduces deep reinforcement learning to solve the VNF placement and scheduling problem in industrial internet of things, utilizing both single and multi-agent schemes to optimize VNF cost and age of information under the constraint of network resources, achieving good results.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Zhongming Ji et al.
Summary: This paper proposes a novel learning scheme called edge-assisted federated learning (EAFL) to mitigate the straggler effect in federated learning by leveraging edge computing. It optimizes the offloading data size and presents a threshold-based offloading strategy, as well as an optimization solution for a dynamic scenario. Simulation results demonstrate that EAFL outperforms the original federated learning scheme in terms of system delay.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Dinh C. Nguyen et al.
Summary: The article discusses the concept of FLchain in MEC networks, focusing on privacy protection, security, cross-device collaboration, and resource allocation. FLchain integrates FL and blockchain technology, presenting a promising paradigm for intelligent MEC networks.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Ying He et al.
Summary: Machine learning algorithms play a crucial role in autonomous driving, but data privacy and security issues have become prominent in connected and autonomous vehicles. Federated learning is introduced for data security, but remains vulnerable to malicious attacks. To address this, Bift is proposed as a decentralized ML system with blockchain integration, offering privacy-preserving ML process for CAVs and defense against attacks.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Proceedings Paper
Computer Science, Cybernetics
Xiumei Deng et al.
Summary: This research proposes a novel blockchain-assisted federated learning framework that integrates training and mining at the client side to address vulnerabilities in existing frameworks. Dynamic training client scheduling and optimization of resource allocation achieve more efficient learning performance.
IEEE CONGRESS ON CYBERMATICS / 2021 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS (ITHINGS) / IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) / IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) / IEEE SMART DATA (SMARTDATA)
(2021)
Article
Computer Science, Information Systems
Chit Wutyee Zaw et al.
Summary: The article presents an energy-aware resource management approach for MEC-enabled FL, aiming to minimize both model training loss and total time consumption while considering the energy limitation of mobile devices. The problem is formulated as a Generalized Nash Equilibrium Problem (GNEP), capturing the coupling constraints between radio resource management and dataset offloading. By analyzing the impact of dataset offloading and computing resource allocation on model training loss, time, and energy consumption, the proposed solution demonstrates efficacy against traditional FL methods through convergence analysis and simulation results.
Article
Computer Science, Information Systems
Dinh C. Nguyen et al.
Summary: The Internet of Things and artificial intelligence are increasingly intertwined in our daily lives, with Federated Learning emerging as a distributed collaborative AI approach that can play a vital role in intelligent IoT applications. This article provides a comprehensive survey of the emerging applications of FL in IoT networks and highlights its potential in various fields, paving the way for future research directions.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2021)
Article
Computer Science, Hardware & Architecture
Canh T. Dinh et al.
Summary: The paper introduces a Federated Learning algorithm called FEDL, which can handle heterogeneous data from mobile user equipment and is applied as a resource allocation optimization problem in wireless networks. Experimental results demonstrate that in various settings, FEDL outperforms the original FedAvg algorithm in terms of convergence rate and test accuracy.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2021)
Article
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IEEE COMMUNICATIONS LETTERS
(2020)
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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2020)
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IEEE TRANSACTIONS ON COMMUNICATIONS
(2020)
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Dinh C. Nguyen et al.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2020)
Article
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Fan Meng et al.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2020)
Article
Computer Science, Information Systems
Haodong Lu et al.
IEEE INTERNET OF THINGS JOURNAL
(2020)
Article
Engineering, Electrical & Electronic
Shiqiang Wang et al.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2019)
Article
Computer Science, Theory & Methods
Lin Xiao et al.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2007)
Article
Automation & Control Systems
L Xiao et al.
SYSTEMS & CONTROL LETTERS
(2004)