Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning
The scalability and performance of edge federated learning are fundamentally constrained by synchronization bottlenecks. Clients operate under heterogeneous compute, time-varying communication conditions, and, in some settings, privacy constraints, yet synchronization is often still handled with fixed schedules. However, synchronization decisions are often made outside the client, even though the client directly observes local progress, resource state, and communication conditions. We present a client-side reinforcement learning synchronization framework in which each client decides during training whether to upload, download, synchronize bidirectionally, or defer communication. The frame...
| Authors | Ebasa Temesgen, Ammar Ahmed, Nathan Lechamo et al. |
|---|---|
| Primary category | cs.DC |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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