Optimizing Resource Allocation and Topology Design for Efficient Hierarchical Federated Edge Learning

The authors propose an optimization-based approach to jointly optimize the edge backhaul topology and resource allocation for a two-tier hierarchical federated edge learning (HFEL) system, considering both system and data heterogeneity, in order to minimize the total training latency while maintaining model accuracy.