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cs.DC2026
Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning
Ebasa Temesgen, Ammar Ahmed, Nathan Lechamo +2
The scalability and performance of edge federated learning are fundamentally constrained by synchronization bottlenecks. Clients operate under heterogeneous compute, time-varying c…
cs.LG2026
Faster Synchronous On-Policy RL via Straggler-Aware Group Sizing
Azal Ahmad Khan, Ammar Ahmed, Zeshan Fayyaz +3
Synchronous reinforcement learning methods such as Group Relative Policy Optimization (GRPO) provide stable and reproducible on-policy training, but they are highly vulnerable to s…