Joint Client Scheduling and Wireless Resource Allocation for Heterogeneous Federated Edge Learning With Non-IID Data
Tong Yin, Lixin Li, Wensheng Lin, Tao Ni, Ying Liu, Haitao Xu, Zhu Han
Abstract
Federated learning (FL) embraces the concepts of targeted data gathering and training, and it can reduce many of the systemic privacy costs and hazards associated with traditional machine learning frameworks. However, with the low latency requirements of the sixth generation (6G) wireless communication networks and the Internet of Things (IoT) networks, the convergence delay of FL dramatically influences the overall system performance. In order to solve this urgent and challenging problem, in this paper, a joint client scheduling and wireless resource allocation algorithm is proposed, named SCSBA, which considers system heterogeneity, client heterogeneity, and the fairness of client participation to reduce the latency resulting from the heterogeneous communication conditions and computation capabilities among clients with the non identically independently distributed (Non-IID) data distributions. Specifically, the Stackelberg leader-follower game is first formulated in which the server decides the price of the single quota of participating in the FL process every communication round and the clients decide whether to participate in FL. Then the equilibrium solution of the game is derived and proved. In addition, a bandwidth allocation algorithm based on the covariance matrix adaptation evolutionary strategy (CMA-ES) is designed to minimize the time delay of each communication round. The simulation results verify the effectiveness of the proposed strategy for reducing the time latency of FL processes with heterogeneous clients, i.e., FedAvg and FedOpt.