Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning
The research addresses how much historical data (memory) each client in federated learning should keep when data distributions drift differently. It proposes giving each client a personalized memory tailored to its specific noise, arrival rate, and drift speed. This leads to better performance than using a single shared window because it separates the forgetting requirements of fast-changing clients from slow ones.