异构边缘计算环境下异步联邦学习的节点分组与分时调度策略OACSCD

To overcome the three key challenges of federated learning in heterogeneous edge computing,i.e.,edge heter-ogeneity,data Non-IID,and communication resource constraints,a grouping asynchronous federated learning(FedGA)mechanism was proposed.Edge nodes were divided into multiple groups,each of which performed global updated asyn-chronously with the global model,while edge nodes within a group communicate with the parameter server through time-sharing communication.Theoretical analysis established a quantitative relationship between the convergence bound of FedGA and the data distribution among the groups.A time-sharing scheduling magic mirror method(MMM)was pro-posed to optimize the completion time of a single round of model updating within a group.Based on both the theoretical analysis for FedGA and MMM,an effective grouping algorithm was designed for minimizing the overall training com-pletion time.Experimental results demonstrate that the proposed FedGA and MMM can reduce model training time by 30.1%～87.4%compared to the existing state-of-the-art methods.