LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915176018608128 |
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| author | Condat, Laurent Maranjyan, Artavazd Richtárik, Peter |
| author_facet | Condat, Laurent Maranjyan, Artavazd Richtárik, Peter |
| contents | In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a communication-efficient algorithm that leverages the two popular and effective techniques of Local training, which reduces the communication frequency, and Compression, in which short bitstreams are sent instead of full-dimensional vectors of floats. LoCoDL works with a large class of unbiased compressors that includes widely-used sparsification and quantization methods. LoCoDL provably benefits from local training and compression and enjoys a doubly-accelerated communication complexity, with respect to the condition number of the functions and the model dimension, in the general heterogenous regime with strongly convex functions. This is confirmed in practice, with LoCoDL outperforming existing algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04348 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Condat, Laurent Maranjyan, Artavazd Richtárik, Peter Optimization and Control Distributed, Parallel, and Cluster Computing Machine Learning In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a communication-efficient algorithm that leverages the two popular and effective techniques of Local training, which reduces the communication frequency, and Compression, in which short bitstreams are sent instead of full-dimensional vectors of floats. LoCoDL works with a large class of unbiased compressors that includes widely-used sparsification and quantization methods. LoCoDL provably benefits from local training and compression and enjoys a doubly-accelerated communication complexity, with respect to the condition number of the functions and the model dimension, in the general heterogenous regime with strongly convex functions. This is confirmed in practice, with LoCoDL outperforming existing algorithms. |
| title | LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression |
| topic | Optimization and Control Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2403.04348 |