FACDVis: 面向联邦学习的异常客户端检测可视分析方法

FACDVis: a visual analysis method for abnormal client detection in Federated Learning

摘要

联邦学习通过隐私保护实现多方数据价值共享,已在医疗、能源等多个领域得到广泛应用,但异常客户端的存在导致联邦学习模型性能受损,系统效率降低。传统异常客户端检测算法依赖于良性客户端占大多数的假设、在应对复杂攻击时易失效,且缺乏可解释性。针对上述问题,提出一种面向联邦学习的异常客户端检测可视分析方法—FACDVis。所提方法首先基于客户端模型性能演化评估体系,实现可疑客户端与异常迭代轮次的初步筛查;其次,通过模型行为模式分析体系,进一步定位异常客户端及其迭代轮次;最后,借助参数异质性诊断体系,深度分析攻击手段,构建可解释的多维证据链检测框架。实验结果表明,该方法能够在异常客户端数量占到80%以上时,仍然有效应对数据投毒、模型投毒等多种攻击手段,识别平均准确率达到94%。

Abstract

Federated Learning enables multi-party data value sharing with privacy protection and has been widely applied in many fields such as healthcare and energy. However, the existence of abnormal clients can degrade the model's performance and reduce system efficiency. Traditional abnormal clients detection algorithms rely on the assumption that the majority of clients are benign, which makes them ineffective against complex attacks and lacks interpretability. To address these issues, a visual analysis method for abnormal client detection in Federated Learning, named FACDVis, is proposed. The method first identifies suspicious clients and anomalous training rounds through the model performance evolution evaluation framework. Next, through the model behavior pattern analysis framework, it further locates the abnormal clients and their corresponding iterations. Finally, parameter heterogeneity diagnosis framework is employed to deeply analyze the attack methods and construct an interpretable multidimensional evidence chain detection framework. Experiments demonstrated that the proposed method effectively resolves data poisoning, model poisoning, and other attacks even when the number of abnormal clients exceeds 80%, the average recognition accuracy rate reaches 94%.

关键词

可视化 / 联邦学习 / 异常客户端检测 / 可解释性

Key words

visualization / Federated Learning / abnormal client detection / interpretability

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基金资助

新疆维吾尔自治区重点研发计划项目(2023B01026)

新疆维吾尔自治区“天山英才”创新团队项目(2023TSYCTD0011)

新疆维吾尔自治区“天山英才”领军人才项目(2023TSYCLJ0022)

新疆维吾尔自治区“天山英才”领军人才项目(2024TSYCLJ0039)

新疆“天池英才”引进计划项目()