Federated learning (FL) enables multiple clients to collaboratively train a global machine learning model via a server without sharing their private training data. In traditional FL, the system follows a synchronous approach, where the server waits for model updates from numerous clients before aggregating them to update the global model. However, synchronous FL is hindered by the straggler problem. To address this, the asynchronous FL architecture allows the server to update the global model immediately upon receiving any client's local model update. Despite its advantages, the decentralized nature of asynchronous FL makes it vulnerable to poisoning attacks. Several defenses tailored for asynchronous FL have been proposed, but these mechanisms remain susceptible to advanced attacks or rely on unrealistic server assumptions. In this paper, we introduce SecureAFL, an innovative framework designed to secure asynchronous FL against poisoning attacks. SecureAFL improves the robustness of asynchronous FL by detecting and discarding anomalous updates while estimating the contributions of missing clients. Additionally, it utilizes Byzantine-robust aggregation techniques, such as coordinate-wise median, to integrate the received and estimated updates. Extensive experiments on various real-world datasets demonstrate the effectiveness of SecureAFL.

翻译：联邦学习使多个客户端能够在服务器的协调下协作训练全局机器学习模型，而无需共享其私有训练数据。传统联邦学习采用同步方式运行，服务器需等待大量客户端的模型更新后进行聚合，从而更新全局模型。然而，同步联邦学习存在落后者问题。为解决此问题，异步联邦学习架构允许服务器在收到任意客户端的局部模型更新后立即更新全局模型。尽管异步联邦学习具有优势，但其去中心化特性使其易受投毒攻击。针对异步联邦学习已提出若干防御机制，但这些方法仍难以抵御高级攻击，或依赖于不切实际的服务器假设。本文提出SecureAFL——一种旨在保护异步联邦学习免受投毒攻击的创新框架。SecureAFL通过检测并丢弃异常更新，同时估计缺失客户端的贡献，提升了异步联邦学习的鲁棒性。此外，它利用拜占庭鲁棒聚合技术（如坐标中位数）整合接收到的与估计的更新。在多种真实数据集上的大量实验证明了SecureAFL的有效性。