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Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its scalability is limited by synchronization overhead. Asynchronous federated learning (AFL) alleviates this issue by allowing clients to communicate independently, thereby improvin
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