Exact Penalty Method for Federated Learning

• URL: http://arxiv.org/abs/2208.11231v1
• Date: Tue, 23 Aug 2022 23:33:38 GMT
• Title: Exact Penalty Method for Federated Learning
• Authors: Shenglong Zhou and and Geoffrey Ye Li
• Abstract summary: Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. In this paper, we deploy an exact penalty method to deal with federated learning and propose an algorithm, FedEPM, that enables to tackle four critical issues in federated learning. It is proven to be convergent and testified to have high numerical performance.
• Score: 34.70820239954457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stochastic) gradient descent methods or the alternating direction method of multipliers. In this paper, we deploy an exact penalty method to deal with federated learning and propose an algorithm, FedEPM, that enables to tackle four critical issues in federated learning: communication efficiency, computational complexity, stragglers' effect, and data privacy. Moreover, it is proven to be convergent and testified to have high numerical performance.

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