Randomized Kaczmarz in Adversarial Distributed Setting
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913263579561984 |
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| author | Huang, Longxiu Li, Xia Needell, Deanna |
| author_facet | Huang, Longxiu Li, Xia Needell, Deanna |
| contents | Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems. In this paper, we propose an iterative approach that is adversary-tolerant for convex optimization problems. By leveraging simple statistics, our method ensures convergence and is capable of adapting to adversarial distributions. Additionally, the efficiency of the proposed methods for solving convex problems is shown in simulations with the presence of adversaries. Through simulations, we demonstrate the efficiency of our approach in the presence of adversaries and its ability to identify adversarial workers with high accuracy and tolerate varying levels of adversary rates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_14615 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Randomized Kaczmarz in Adversarial Distributed Setting Huang, Longxiu Li, Xia Needell, Deanna Optimization and Control Cryptography and Security Machine Learning Numerical Analysis 65F20, 65F10, 65K10 Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems. In this paper, we propose an iterative approach that is adversary-tolerant for convex optimization problems. By leveraging simple statistics, our method ensures convergence and is capable of adapting to adversarial distributions. Additionally, the efficiency of the proposed methods for solving convex problems is shown in simulations with the presence of adversaries. Through simulations, we demonstrate the efficiency of our approach in the presence of adversaries and its ability to identify adversarial workers with high accuracy and tolerate varying levels of adversary rates. |
| title | Randomized Kaczmarz in Adversarial Distributed Setting |
| topic | Optimization and Control Cryptography and Security Machine Learning Numerical Analysis 65F20, 65F10, 65K10 |
| url | https://arxiv.org/abs/2302.14615 |