In the light of the growing connectivity and sensitivity of industrial data, cyberattacks and data breaches are becoming more common in the Industrial Internet of Things (IIoT). To cope with such threats, this study presents an anomaly detection system based on a novel Federated Learning (FL) framework. This system detects anomalies such as cyberattacks and protects industrial data privacy by processing data locally and training anomaly detection models on industrial agents without sharing raw data. The proposed FL framework incorporates two key components to enhance both privacy and efficiency. The first component is Homomorphic Encryption (HE), which is integrated into the framework to further protect sensitive data transmissions such as model parameters. HE enhances privacy in FL by preventing adversaries from inferring private industrial data through attacks, such as model inversion attacks. The second component is an innovative dynamic agent selection scheme, wherein a selection threshold is calculated based on agent delays and data size. The purpose of this new scheme is to mitigate the straggler effect and the communication bottleneck that occur in traditional FL architectures, such as synchronous and asynchronous architectures. It ensures that agents are not unfairly selected by the different delays resulting from heterogeneous data in IIoT environments, while simultaneously improving model performance and convergence speed. The proposed framework exhibits superior performance over baseline approaches in terms of accuracy, precision, F1-scores, communication costs, convergence speeds, and fairness rate.

翻译：鉴于工业数据日益增长的互联性和敏感性，网络攻击与数据泄露在工业物联网中愈发常见。为应对此类威胁，本研究提出了一种基于新型联邦学习框架的异常检测系统。该系统通过本地处理数据并在工业节点上训练异常检测模型（无需共享原始数据），能够检测网络攻击等异常行为并保护工业数据隐私。所提出的联邦学习框架整合了两大核心组件以增强隐私性与效率：第一，同态加密组件——将其融入框架以进一步保护模型参数等敏感数据的传输，通过防止攻击者利用模型反演攻击等手段推断私有工业数据来强化联邦学习的隐私保护能力；第二，创新的动态节点选择方案——基于节点延迟与数据量计算选择阈值。该方案旨在缓解传统联邦学习架构（如同步与异步架构）中存在的掉队者效应与通信瓶颈问题，确保节点不会因工业物联网环境中异构数据导致的时延差异而被不公平地选中，同时提升模型性能与收敛速度。实验结果表明，所提框架在准确率、精确率、F1分数、通信开销、收敛速度及公平性指标上均优于基准方法。