DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914636279840768 |
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| author | Park, Jongho Xu, Jinchao |
| author_facet | Park, Jongho Xu, Jinchao |
| contents | We propose a new training algorithm, named DualFL (Dualized Federated Learning), for solving distributed optimization problems in federated learning. DualFL achieves communication acceleration for very general convex cost functions, thereby providing a solution to an open theoretical problem in federated learning concerning cost functions that may not be smooth nor strongly convex. We provide a detailed analysis for the local iteration complexity of DualFL to ensure the overall computational efficiency of DualFL. Furthermore, we introduce a completely new approach for the convergence analysis of federated learning based on a dual formulation. This new technique enables concise and elegant analysis, which contrasts the complex calculations used in existing literature on convergence of federated learning algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_10294 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime Park, Jongho Xu, Jinchao Machine Learning Optimization and Control 68W15, 90C46, 90C25 We propose a new training algorithm, named DualFL (Dualized Federated Learning), for solving distributed optimization problems in federated learning. DualFL achieves communication acceleration for very general convex cost functions, thereby providing a solution to an open theoretical problem in federated learning concerning cost functions that may not be smooth nor strongly convex. We provide a detailed analysis for the local iteration complexity of DualFL to ensure the overall computational efficiency of DualFL. Furthermore, we introduce a completely new approach for the convergence analysis of federated learning based on a dual formulation. This new technique enables concise and elegant analysis, which contrasts the complex calculations used in existing literature on convergence of federated learning algorithms. |
| title | DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime |
| topic | Machine Learning Optimization and Control 68W15, 90C46, 90C25 |
| url | https://arxiv.org/abs/2305.10294 |