  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.
