Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866913272551178240 |
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| author | Tang, Tingting Ali, Ramy E. Hashemi, Hanieh Gangwani, Tynan Avestimehr, Salman Annavaram, Murali |
| author_facet | Tang, Tingting Ali, Ramy E. Hashemi, Hanieh Gangwani, Tynan Avestimehr, Salman Annavaram, Murali |
| contents | Stragglers, Byzantine workers, and data privacy are the main bottlenecks in distributed cloud computing. Some prior works proposed coded computing strategies to jointly address all three challenges. They require either a large number of workers, a significant communication cost or a significant computational complexity to tolerate Byzantine workers. Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework. In this paper, we propose Adaptive Verifiable Coded Computing (AVCC) framework that decouples the Byzantine node detection challenge from the straggler tolerance. AVCC leverages coded computing just for handling stragglers and privacy, and then uses an orthogonal approach that leverages verifiable computing to mitigate Byzantine workers. Furthermore, AVCC dynamically adapts its coding scheme to trade-off straggler tolerance with Byzantine protection. We evaluate AVCC on a compute-intensive distributed logistic regression application. Our experiments show that AVCC achieves up to $4.2\times$ speedup and up to $5.1\%$ accuracy improvement over the state-of-the-art Lagrange coded computing approach (LCC). AVCC also speeds up the conventional uncoded implementation of distributed logistic regression by up to $7.6\times$, and improves the test accuracy by up to $12.1\%$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_12958 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning Tang, Tingting Ali, Ramy E. Hashemi, Hanieh Gangwani, Tynan Avestimehr, Salman Annavaram, Murali Distributed, Parallel, and Cluster Computing Cryptography and Security Information Theory Machine Learning Stragglers, Byzantine workers, and data privacy are the main bottlenecks in distributed cloud computing. Some prior works proposed coded computing strategies to jointly address all three challenges. They require either a large number of workers, a significant communication cost or a significant computational complexity to tolerate Byzantine workers. Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework. In this paper, we propose Adaptive Verifiable Coded Computing (AVCC) framework that decouples the Byzantine node detection challenge from the straggler tolerance. AVCC leverages coded computing just for handling stragglers and privacy, and then uses an orthogonal approach that leverages verifiable computing to mitigate Byzantine workers. Furthermore, AVCC dynamically adapts its coding scheme to trade-off straggler tolerance with Byzantine protection. We evaluate AVCC on a compute-intensive distributed logistic regression application. Our experiments show that AVCC achieves up to $4.2\times$ speedup and up to $5.1\%$ accuracy improvement over the state-of-the-art Lagrange coded computing approach (LCC). AVCC also speeds up the conventional uncoded implementation of distributed logistic regression by up to $7.6\times$, and improves the test accuracy by up to $12.1\%$. |
| title | Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning |
| topic | Distributed, Parallel, and Cluster Computing Cryptography and Security Information Theory Machine Learning |
| url | https://arxiv.org/abs/2107.12958 |