Composite Federated Learning with Heterogeneous Data | IEEE Conference Publication | IEEE Xplore
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Composite Federated Learning with Heterogeneous Data


Abstract:

We propose a novel algorithm for solving the composite Federated Learning (FL) problem. This algorithm manages non-smooth regularization by strategically decoupling the p...Show More

Abstract:

We propose a novel algorithm for solving the composite Federated Learning (FL) problem. This algorithm manages non-smooth regularization by strategically decoupling the proximal operator and communication, and addresses client drift without any assumptions about data similarity. Moreover, each worker uses local updates to reduce the communication frequency with the server and transmits only a d-dimensional vector per communication round. We prove that our algorithm converges linearly to a neighborhood of the optimal solution and demonstrate the superiority of our algorithm over state-of-the-art methods in numerical experiments.
Date of Conference: 14-19 April 2024
Date Added to IEEE Xplore: 18 March 2024
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Conference Location: Seoul, Korea, Republic of

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