Accelerating Federated Edge Learning via Topology Optimization
Shanfeng Huang, Zezhong Zhang, Shuai Wang, Rui Wang, Kaibin Huang
Abstract
Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices. In this article, a novel topology-optimized FEEL (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To solve the mixed-integer nonlinear problem, we propose a novel solution method of penalty-based successive convex approximation (SCA), which converges to a stationary point of the primal problem under mild conditions. To facilitate real-time decision making, an imitation-learning-based method is developed, where deep neural networks (DNNs) are trained offline to mimic the penalty-based method, and the trained imitation DNNs are deployed at the edge devices for online inference. Thereby, an efficient imitation-learning-based approach is seamlessly integrated into the TOFEL framework. Simulation results demonstrate that the proposed TOFEL scheme accelerates the federated learning process and achieves a higher energy efficiency. Moreover, we apply the scheme to 3-D object detection with multivehicle point cloud data sets in the CARLA simulator. The results confirm the superior learning performance of the TOFEL scheme over conventional designs with the same resource and deadline constraints.