Network for AI: Communication-Efficient Federated Learning with MST-based Scheduling and Multi-Aggregation over Optical Networks

We propose a Minimum-Spanning-Tree-based scheduling and Multi-aggregation framework (MST-M) for communication-efficient Federated Learning. Simulation results show that MST-M saves over 10% in communication costs compared to existing heuristics.