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Found 2 papers, 1 papers with code
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update
This approach ensures that a portion of the local model remains personalized, thereby enhancing the model's robustness against biased parameters from other clients.
FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy
Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data.
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