FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs

This paper introduces FedSecurity, an end-to-end benchmark designed to simulate adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). FedSecurity comprises two pivotal components: FedAttacker, which facilitates the simulation of a variety of attacks during FL training, and FedDefender, which implements defensive mechanisms to counteract these attacks. As an open-source library, FedSecurity enhances its usability compared to from-scratch implementations that focus on specific attack/defense scenarios based on the following features: i) It offers extensive customization options to accommodate a …

adversarial adversarial attacks attacks benchmark components cs.ai cs.cr defense defenses defensive end end-to-end federated federated learning llms simulation training