FedRef uses a temporally aggregated reference model and MAP regularization for server-side fine-tuning to reduce forgetting and drift in non-IID federated learning, showing better accuracy and lower client compute on image tasks.
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FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
FedRef uses a temporally aggregated reference model and MAP regularization for server-side fine-tuning to reduce forgetting and drift in non-IID federated learning, showing better accuracy and lower client compute on image tasks.