Related papers: A Primal-Dual Algorithm for Hybrid Federated Learning

A Primal-Dual Algorithm for Hybrid Federated Learning

URL: http://arxiv.org/abs/2210.08106v3
Date: Fri, 9 Feb 2024 18:21:39 GMT
Title: A Primal-Dual Algorithm for Hybrid Federated Learning
Authors: Tom Overman, Garrett Blum, Diego Klabjan
Abstract summary: We provide a fast, robust algorithm for hybrid federated learning that hinges on Fenchel Duality. We also provide privacy considerations and necessary steps to protect client data.
Score: 11.955062839855334
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Very few methods for hybrid federated learning, where clients only hold subsets of both features and samples, exist. Yet, this scenario is extremely important in practical settings. We provide a fast, robust algorithm for hybrid federated learning that hinges on Fenchel Duality. We prove the convergence of the algorithm to the same solution as if the model is trained centrally in a variety of practical regimes. Furthermore, we provide experimental results that demonstrate the performance improvements of the algorithm over a commonly used method in federated learning, FedAvg, and an existing hybrid FL algorithm, HyFEM. We also provide privacy considerations and necessary steps to protect client data.

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