Adaptive Retransmission Design for Wireless Federated Edge Learning  

作　　者：XU Xinyi LIU Shengli YU Guanding 

机构地区：[1]Zhejiang University,Hangzhou 310027,China

出　　处：《ZTE Communications》2023年第1期3-14,共12页中兴通讯技术（英文版）

摘　　要：As a popular distributed machine learning framework,wireless federated edge learning(FEEL)can keep original data local,while uploading model training updates to protect privacy and prevent data silos.However,since wireless channels are usually unreliable,there is no guarantee that the model updates uploaded by local devices are correct,thus greatly degrading the performance of the wireless FEEL.Conventional retransmission schemes designed for wireless systems generally aim to maximize the system throughput or minimize the packet error rate,which is not suitable for the FEEL system.A novel retransmission scheme is proposed for the FEEL system to make a tradeoff between model training accuracy and retransmission latency.In the proposed scheme,a retransmission device selection criterion is first designed based on the channel condition,the number of local data,and the importance of model updates.In addition,we design the air interface signaling under this retransmission scheme to facilitate the implementation of the proposed scheme in practical scenarios.Finally,the effectiveness of the proposed retransmission scheme is validated through simulation experiments.

关 键 词：federated edge learning RETRANSMISSION unreliable communication convergence rate retransmission latency 

分 类 号：TN92[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程]