Abstract

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 …

Metadata

publication

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024

year

2024

publication date

2024/8/24

authors

Shanshan Han, Baturalp Buyukates, Zijian Hu, Han Jin, Weizhao Jin, Lichao Sun, Xiaoyang Wang, Chulin Xie, Kai Zhang, Qifan Zhang, Yuhui Zhang, Chaoyang He, Salman Avestimehr

link

https://dl.acm.org/doi/abs/10.1145/3637528.3671545

resource_link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671545

conference

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining