This paper introduces FedMLSecurity, a benchmark designed to simulate adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). As an integral module of the open-sourced library FedML that facilitates FL algorithm development and performance comparison, FedMLSecurity enhances FedML's capabilities to evaluate security issues and potential remedies in FL. FedMLSecurity comprises two major components: FedMLAttacker that simulates attacks injected during FL training, and FedMLDefender that simulates defensive mechanisms to mitigate the impacts of the attacks. FedMLSecurity is open-sourced and can be customized to a wide range of machine learning models (e.g., Logistic Regression, ResNet, GAN, etc.) and federated optimizers (e.g., FedAVG, FedOPT, FedNOVA, etc.). FedMLSecurity can also be applied to Large Language Models (LLMs) easily, demonstrating its adaptability and applicability in various scenarios.

翻译：本文介绍了FedMLSecurity，一个旨在模拟联邦学习（FL）中对抗攻击及相应防御机制的基准测试框架。作为开源库FedML的核心模块，FedMLSecurity不仅支持FL算法开发与性能对比，更通过增强FedML的能力，系统评估FL中的安全问题及潜在解决方案。FedMLSecurity包含两大核心组件：FedMLAttacker模拟FL训练过程中注入的攻击行为，而FedMLDefender则模拟用于缓解攻击影响的防御机制。该基准测试框架已开源，可针对多种机器学习模型（如逻辑回归、ResNet、GAN等）及联邦优化器（如FedAVG、FedOPT、FedNOVA等）进行定制。此外，FedMLSecurity还可便捷地应用于大语言模型（LLMs），充分展现其在不同场景下的适应性与扩展性。