A credibility-aware swarm-federated deep learning framework in internet of vehicles  

作　　者：Zhe Wang Xinhang Li Tianhao Wu Chen Xu Lin Zhang 

机构地区：[1]School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing,100876,China

出　　处：《Digital Communications and Networks》2024年第1期150-157,共8页数字通信与网络（英文版）

基　　金：supported by the National Natural Science Foundation of China(NSFC)under Grant 62071179.

摘　　要：Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

关 键 词：Swarm learning Federated deep learning Internet of vehicles PRIVACY EFFICIENCY 

分 类 号：TN929.5[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程] U495[自动化与计算机技术—控制理论与控制工程]