典型文献
Time Efficient Joint Optimization Federated Learning over Wireless Communication Networks
文献摘要:
Artificial intelligence(AI)has made a pro-found impact on our daily life.The 6th generation mobile networks(6G)should be designed to enable AI services.The native intelligence is introduced as an important feature in 6G.6G native AI network is realized by the philosophy of federated learning(FL)to ensure data security and privacy.Federated learning over wireless communication networks is treated as a potential solution to realize native AI.However,intro-ducing FL in the 6G will lead to expansive communi-cation cost and unstable FL convergence with unreli-able air interface.In this paper,we propose a solution for FL over wireless networks and analyze the training efficiency.To make full use of the advantages of the proposed network,we introduce a communication-FL joint optimization(CFJO)algorithm by jointly con-sidering the effects of uplink resource,energy con-sumption and latency constraints.CFJO derives a transmission strategy with resource allocation and re-transmissions to reduce the wireless transmission in-terruption probability and model upload latency.The simulation results show that CFJO significantly im-proves the model training efficiency and convergence performance with lower interruption probability under the latency constraint.
文献关键词:
中图分类号:
作者姓名:
Junshuai Sun;Yingying Wang;Xin Sun;Na Li;Gaofeng Nie
作者机构:
School of Computer Science and Technology,Xidian University,Xi'an 7100716,China;China Mobile Research Institute,Beijing 100053,China;Beijing University of Posts and Telecommunications,Beijing 100876,China
文献出处:
引用格式:
[1]Junshuai Sun;Yingying Wang;Xin Sun;Na Li;Gaofeng Nie-.Time Efficient Joint Optimization Federated Learning over Wireless Communication Networks)[J].中国通信(英文版),2022(06):169-178
A类:
unreli,CFJO,terruption
B类:
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AB值:
0.545373
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