FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913631800655872 |
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| author | Yan, Peishen Li, Jun Wang, Hao Song, Tao Hua, Yang Peng, Lu Zhou, Haihui Guan, Haibing |
| author_facet | Yan, Peishen Li, Jun Wang, Hao Song, Tao Hua, Yang Peng, Lu Zhou, Haihui Guan, Haibing |
| contents | Federated Learning (FL) is an innovative distributed machine learning paradigm that enables multiple parties to collaboratively train a model without sharing their raw data, thereby preserving data privacy. Communication efficiency concerns arise in cross-silo FL, particularly due to the network heterogeneity and fluctuations associated with geo-distributed silos. Most existing solutions to these problems focus on algorithmic improvements that alter the FL algorithm but sacrificing the training performance. How to address these problems from a network perspective that is decoupled from the FL algorithm remains an open challenge. In this paper, we propose FedCod, a new application layer communication protocol designed for cross-silo FL. FedCod transparently utilizes a coding mechanism to enhance the efficient use of idle bandwidth through client-to-client communication, and dynamically adjusts coding redundancy to mitigate network bottlenecks and fluctuations, thereby improving the communication efficiency and accelerating the training process. In our real-world experiments, FedCod demonstrates a significant reduction in average communication time by up to 62% compared to the baseline, while maintaining FL training performance and optimizing inter-client communication traffic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00216 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding Yan, Peishen Li, Jun Wang, Hao Song, Tao Hua, Yang Peng, Lu Zhou, Haihui Guan, Haibing Distributed, Parallel, and Cluster Computing Federated Learning (FL) is an innovative distributed machine learning paradigm that enables multiple parties to collaboratively train a model without sharing their raw data, thereby preserving data privacy. Communication efficiency concerns arise in cross-silo FL, particularly due to the network heterogeneity and fluctuations associated with geo-distributed silos. Most existing solutions to these problems focus on algorithmic improvements that alter the FL algorithm but sacrificing the training performance. How to address these problems from a network perspective that is decoupled from the FL algorithm remains an open challenge. In this paper, we propose FedCod, a new application layer communication protocol designed for cross-silo FL. FedCod transparently utilizes a coding mechanism to enhance the efficient use of idle bandwidth through client-to-client communication, and dynamically adjusts coding redundancy to mitigate network bottlenecks and fluctuations, thereby improving the communication efficiency and accelerating the training process. In our real-world experiments, FedCod demonstrates a significant reduction in average communication time by up to 62% compared to the baseline, while maintaining FL training performance and optimizing inter-client communication traffic. |
| title | FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2501.00216 |