Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning
We study federated learning from continuously arriving data: each client samples from its own distribution, these distributions drift over time, and the server must track the minimizer of the clients' weighted average loss. Clients are not alike as they collect data at different rates, with different noise, and change at different speeds. The main question is how much history should each one keep? A single shared window must serve every client at once, and so makes a fast-changing client carry the sampling noise of a slow, nearly stationary one. We give each client a memory of its own, sized to that client's noise, arrival rate, and drift, and refresh it by restarted accelerated method th...
| Authors | Yuriy Dorn, Dmitrii Vishovan, Kirill Novoselov et al. |
|---|---|
| Primary category | math.OC |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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