Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning
The paper addresses synchronization bottlenecks in edge federated learning by introducing a client-side reinforcement learning framework. Clients use a scheduler to decide dynamically whether to upload, download, synchronize bidirectionally, or defer communication based on local learning needs and system resources. This adaptive approach improves the accuracy-cost trade-off across different workloads.