Exact Penalty Method for Federated Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 23 Aug 2022.
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31 of 47 references.
References · 31
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19 more
- 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
- 2018Don't Use Large Mini-Batches, Use Local SGDcited by 202
- 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 201
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- 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 144
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- 2014Deep learning with Elastic Averaging SGDcited by 90
- 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 78
- 2020Federated Learning in Vehicular Networkscited by 53
- 2022Federated Learning via Inexact ADMMcited by 44
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- 2020Federated Learning and Wireless Communicationscited by 27
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- 2021Differentially Private Federated Learning via Inexact ADMMcited by 11
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- 2022Federated Stochastic Primal-dual Learning with Differential Privacycited by 5
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