TOFEL: A topology-based federated edge learning scheme
Accelerating Federated Edge Learning via Topology Optimization
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 paper, a novel topology-optimized federated edge learning (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.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 3d object detection with multi-vehicle point cloud datasets in the carla simulator.The results confirm the superior learning performance of the proposed scheme over conventional designs with the same resource and deadline constraints.
