Asynchronous Federated Learning via Blockchain in Edge Computing Networks
Because of the information explosion caused by the surge of data,traditional centralized cloud computing is overwhelmed,Edge Computing Network(ECN)is proposed to alleviate the burden on cloud servers.In contrast,by permitting Federated Learning(FL)in the ECN,data localization processing could be realized to successfully address the data security problem of Edge Nodes(ENs)in collaborative learning.However,traditional FL exposes the central server to single-point attacks,resulting in system performance degradation or even task failure.In this paper,we propose Asynchronous Federated Learning based on Blockchain technology(AFLChain)in the ECN that can dynamically assign learning tasks to ENs based on their computing capabilities to boost learning efficiency.In addition,based on the computing capability of ENs,model training progress and historical reputation,the entropy weight reputation mechanism is implemented to assess and rank the enthusiasm of ENs,eliminating low quality ENs to further improve the performance of the AFLChain.Finally,the Subgradient based Optimal Resource Allocation(SORA)algorithm is proposed to reduce network latency by optimizing transmission power and computing resource allocation simultaneously.The simulation results demonstrate the model training efficiency of the AFLChain and the convergence of the SORA algorithm and the efficacy of the proposed algorithms.
万方数据
维普
