BoBa：通过联邦学习中的数据分布推断提升后门检测
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
AI总结：
本文提出BoBa机制，通过数据分布聚类和投票检测提升联邦学习中后门攻击检测的鲁棒性，有效降低攻击成功率至0.001以下。
摘要：
联邦学习虽然在协同模型训练中具有前景，但其去中心化性质使其易受后门攻击。后门攻击具有高度隐蔽性，仅在输入包含特定触发器时才会破坏模型预测。为应对这一问题，本文提出一种分布感知的后门检测机制BoBa。通过将问题分解为两个步骤：利用数据分布对客户端进行聚类，随后进行基于投票的检测。我们提出了一种新的数据分布推断机制以准确估计数据分布。为提高检测鲁棒性，我们引入了重叠聚类方法，其中每个客户端与多个聚类相关联，确保模型更新的可信度由多个聚类共同评估而非单一聚类。通过广泛评估，我们证明BoBa可以在各种攻击策略和实验设置中将攻击成功率降低到低于0.001，同时保持高主任务准确性。
英文摘要：
Federated learning, while being a promising approach for collaborative model training, is susceptible to backdoor attacks due to its decentralized nature. Backdoor attacks have shown remarkable stealthiness, as they compromise model predictions only when inputs contain specific triggers. As a countermeasure, anomaly detection is widely used to filter out backdoor attacks in FL. However, the non-independent and identically distributed (non-IID) data distribution nature of FL clients presents substantial challenges in backdoor attack detection, as the data variety introduces variance among benign models, making them indistinguishable from malicious ones. In this work, we propose a novel distribution-aware backdoor detection mechanism, BoBa, to address this problem. To differentiate outliers arising from data variety versus backdoor attacks, we propose to break down the problem into two steps: clustering clients utilizing their data distribution, and followed by a voting-based detection. We propose a novel data distribution inference mechanism for accurate data distribution estimation. To improve detection robustness, we introduce an overlapping clustering method, where each client is associated with multiple clusters, ensuring that the trustworthiness of a model update is assessed collectively by multiple clusters rather than a single cluster. Through extensive evaluations, we demonstrate that BoBa can reduce the attack success rate to lower than 0.001 while maintaining high main task accuracy across various attack strategies and experimental settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
