Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning

Name: Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
Uploaded: 2026-10-15T20:06:57.333542+00:00
Duration: 263 s
Description: FedRevive is an asynchronous federated learning framework that improves training speed and accuracy by reviving stale client updates using data-free knowledge distillation. It achieves up to 38.4% faster training and 16.5% higher accuracy than standard asynchronous methods by synthesizing pseudo-samples from old models.

FedRevive is an asynchronous federated learning framework that improves training speed and accuracy by reviving stale client updates using data-free knowledge distillation. It achieves up to 38.4% faster training and 16.5% higher accuracy than standard asynchronous methods by synthesizing pseudo-samples from old models.

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