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Towards asynchronous federated learning for heterogeneous edge-powered internet of things

Digital Communications and Networks 2021年第3期7卷 317-326页

作者： Zheyi Chen Weixian Liao Kun Hua Chao Lu Wei Yu Department of Computer and Information Sciences Towson UniversityTowsonMD21252USA Department of Electrical and Computer Engineering Lawrence Technological UniversitySouthfieldMI48075USA

The advancement of the Internet of Things(IoT)brings new opportunities for collecting real-time data and deploying machine learning ***,an individual IoT device may not have adequate computing resources to train and deploy an entire learning *** the same time,transmitting continuous real-time data to a central server with high computing resource incurs enormous communication costs and raises issues in data security and *** learning,a distributed machine learning framework,is a promising solution to train machine learning models with resource-limited devices and edge ***,the majority of existing works assume an impractically synchronous parameter update manner with homogeneous IoT nodes under stable communication *** this paper,we develop an asynchronous federated learning scheme to improve training efficiency for heterogeneous IoT devices under unstable communication ***,we formulate an asynchronous federated learning model and develop a lightweight node selection algorithm to carry out learning tasks *** proposed algorithm iteratively selects heterogeneous IoT nodes to participate in the global learning aggregation while considering their local computing resource and communication *** experimental results demonstrate that our proposed asynchronous federated learning scheme outperforms the state-of-the-art schemes in various settings on independent and identically distributed(i.i.d.)and *** distribution.

关键词： asynchronous federated learning Internet of Things(IoT) Mobile edge computing

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A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for asynchronous federated learning

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Computers, Materials & Continua 2023年第5期75卷 2939-2955页

作者： Zerui Zhen Zihao Wu Lei Feng Wenjing Li Feng Qi Shixuan Guo Beijing University of Posts and Telecommunication Beijing100876China Vanderbilt University Nashville TN37240USA

asynchronous federated learning(AsynFL)can effectivelymitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data ***,the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning(FL)algorithm uses synchronous or asynchronous ***,there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption *** paper proposes a novel Fixed-point asynchronous federated learning(FixedAsynFL)algorithm,which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device *** uses fixed-point quantization to compress the local and global models in *** order to balance energy consumption and learning accuracy,this paper proposed a quantization scale selection *** paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the *** on considering the upper bound of quantization noise,this paper optimizes the quantization scale by minimizing communication and computation *** paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing *** results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3%and save the computation energy in the training phase by 74.9%without significant loss of *** to the experimental results,we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes.

关键词： asynchronous federated learning artificial intelligence model compression energy consumption fixed-point quantization learning accuracy

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Anti-Byzantine Attacks Enabled Vehicle Selection for asynchronous federated learning in Vehicular Edge Computing

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China Communications 2024年第8期21卷 1-17页

作者： Zhang Cui Xu Xiao Wu Qiong Fan Pingyi Fan Qiang Zhu Huiling Wang Jiangzhou School of Internet of Things Engineering Wuxi Institute of TechnologyWuxi 214121China School of Internet of Things Engineering Jiangnan UniversityWuxi 214122China Department of Electronic Engineering Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Qualcomm San Jose CA 95110USA School of Engineering University of KentCT27NT CanterburyU.K

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation *** to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is *** above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle ***,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before *** the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the *** this paper,we proposed a vehicle selection scheme based on DRL in *** this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into *** results show that the proposed scheme effectively improves the safety and accuracy of the global model.

关键词： asynchronous federated learning byzantine attacks vehicle selection vehicular edge computing

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A Double-Compensation-Based federated learning Scheme for Data Privacy Protection in a Social IoT Scenario

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Computers, Materials & Continua 2023年第7期76卷 827-848页

作者： Junqi Guo Qingyun Xiong Minghui Yang Ziyun Zhao School of Artificial Intelligence Beijing Normal UniversityBeijing100875China Engineering Research Center of Intelligent Technology and Educational Application Ministry of EducationBeijing100875China

Nowadays,smart wearable devices are used widely in the Social Internet of Things(IoT),which record human physiological data in real *** protect the data privacy of smart devices,researchers pay more attention to federated *** the data leakage problem is somewhat solved,a new challenge has *** federated learning shortens the convergence time,while it has time delay and data heterogeneity *** of the two problems harm the *** overcome these issues,we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time delay and data heterogeneity *** scheme improves the Delay Compensated asynchronous Stochastic Gradient Descent(DC-ASGD)algorithm based on the second-order Taylor expansion as the delay *** adds the FedProx operator to the objective function as the heterogeneity ***,the proposed scheme motivates the federated learning process by adjusting the importance of the participants and the central *** conduct multiple sets of experiments in both conventional and heterogeneous *** experimental results show that our scheme improves the accuracy by about 5%while keeping the complexity *** can find that our scheme converges more smoothly during training and adapts better in heterogeneous environments through numerical *** proposed double-compensation-based federated learning scheme is highly accurate,flexible in terms of participants and smooth the training *** it is deemed suitable for data privacy protection of smart wearable devices.

关键词： Social Internet of Things smart wearable devices data privacy asynchronous federated learning

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