Pytorch Federated Learning Tutorial

Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset

Abstract: Federated Learning is a distributed machine learning paradigm that enables model training across decentralized devices holding local data, thereby preserving data privacy and reducing the ...

IEEE

A Novel Approach for Differential Privacy-Preserving Federated Learning

Abstract: In this paper, we start with a comprehensive evaluation of the effect of adding differential privacy (DP) to federated learning (FL) approaches, focusing on methodologies employing global ...

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Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset

A Novel Approach for Differential Privacy-Preserving Federated Learning

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