FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911928110022656 |
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| author | Han, Shanshan Buyukates, Baturalp Hu, Zijian Jin, Han Jin, Weizhao Sun, Lichao Wang, Xiaoyang Wu, Wenxuan Xie, Chulin Yao, Yuhang Zhang, Kai Zhang, Qifan Zhang, Yuhui Joe-Wong, Carlee Avestimehr, Salman He, Chaoyang |
| author_facet | Han, Shanshan Buyukates, Baturalp Hu, Zijian Jin, Han Jin, Weizhao Sun, Lichao Wang, Xiaoyang Wu, Wenxuan Xie, Chulin Yao, Yuhang Zhang, Kai Zhang, Qifan Zhang, Yuhui Joe-Wong, Carlee Avestimehr, Salman He, Chaoyang |
| contents | This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). FedSecurity eliminates the need for implementing the fundamental FL procedures, e.g., FL training and data loading, from scratch, thus enables users to focus on developing their own attack and defense strategies. It contains two key components, including FedAttacker that conducts a variety of attacks during FL training, and FedDefender that implements defensive mechanisms to counteract these attacks. FedSecurity has the following features: i) It offers extensive customization options to accommodate a broad range of machine learning models (e.g., Logistic Regression, ResNet, and GAN) and FL optimizers (e.g., FedAVG, FedOPT, and FedNOVA); ii) it enables exploring the effectiveness of attacks and defenses across different datasets and models; and iii) it supports flexible configuration and customization through a configuration file and some APIs. We further demonstrate FedSecurity's utility and adaptability through federated training of Large Language Models (LLMs) to showcase its potential on a wide range of complex applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_04959 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs Han, Shanshan Buyukates, Baturalp Hu, Zijian Jin, Han Jin, Weizhao Sun, Lichao Wang, Xiaoyang Wu, Wenxuan Xie, Chulin Yao, Yuhang Zhang, Kai Zhang, Qifan Zhang, Yuhui Joe-Wong, Carlee Avestimehr, Salman He, Chaoyang Cryptography and Security Artificial Intelligence This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). FedSecurity eliminates the need for implementing the fundamental FL procedures, e.g., FL training and data loading, from scratch, thus enables users to focus on developing their own attack and defense strategies. It contains two key components, including FedAttacker that conducts a variety of attacks during FL training, and FedDefender that implements defensive mechanisms to counteract these attacks. FedSecurity has the following features: i) It offers extensive customization options to accommodate a broad range of machine learning models (e.g., Logistic Regression, ResNet, and GAN) and FL optimizers (e.g., FedAVG, FedOPT, and FedNOVA); ii) it enables exploring the effectiveness of attacks and defenses across different datasets and models; and iii) it supports flexible configuration and customization through a configuration file and some APIs. We further demonstrate FedSecurity's utility and adaptability through federated training of Large Language Models (LLMs) to showcase its potential on a wide range of complex applications. |
| title | FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2306.04959 |