Federated learning has emerged as a promising approach to enable collaborative machine learning among multiple parties while keeping their data private. However, FL also presents new challenges in terms of security and robustness. To address these concerns, researchers have developed FedSecurity, an end-to-end benchmark designed to simulate adversarial attacks and corresponding defense mechanisms. This comprehensive benchmark eliminates the need for implementing fundamental FL procedures from scratch, allowing users to focus on developing their own attack and defense strategies.
FedSecurity offers extensive customization options to accommodate a broad range of machine learning models and FL optimizers, providing users with the flexibility to explore the effectiveness of attacks and defenses across different datasets and models. The benchmark's ability to simulate attacks and defenses across various scenarios enables researchers to evaluate the robustness of their approaches in different conditions.
With its flexible configuration and customization options, FedSecurity provides a valuable tool for researchers working in the field of federated learning, enabling them to develop a deeper understanding of their attack and defense strategies in various scenarios.
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