Abstract: Federated learning enables the privacy-preserving training of neural network models using real-world data across distributed clients. FedAvg has become the preferred optimizer for federated ...
Abstract: Secure aggregation becomes a major solution to providing privacy for federated learning. Secure aggregation for mobile devices typically relies on Shamir secret sharing (SSS) to achieve ...
Abstract: The performance of Federated Learning (FL) hinges on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where ...
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