A Novel Buffered Federated Learning Framework for Privacy-Driven Anomaly Detection in IIoT

TL;DR

This work introduces Buffered Federated Learning (BFL), a privacy-preserving FL framework for IIoT anomaly detection that uses a buffer-based server and homomorphic encryption to mitigate straggler effects and communication bottlenecks. A novel weighted average time $T_{W\_Avg}$ balances fast and slow clients, enhancing fairness and convergence in heterogeneous environments. Experiments on Gas pipeline and WUSTL-IIoT datasets show that BFL delivers higher accuracy and faster convergence than state-of-the-art FL methods while preserving data privacy. The approach has practical implications for robust, private anomaly detection in industrial cyber-physical systems and points to dynamic client selection as future work.

Abstract

Industrial Internet of Things (IIoT) is highly sensitive to data privacy and cybersecurity threats. Federated Learning (FL) has emerged as a solution for preserving privacy, enabling private data to remain on local IIoT clients while cooperatively training models to detect network anomalies. However, both synchronous and asynchronous FL architectures exhibit limitations, particularly when dealing with clients with varying speeds due to data heterogeneity and resource constraints. Synchronous architecture suffers from straggler effects, while asynchronous methods encounter communication bottlenecks. Additionally, FL models are prone to adversarial inference attacks aimed at disclosing private training data. To address these challenges, we propose a Buffered FL (BFL) framework empowered by homomorphic encryption for anomaly detection in heterogeneous IIoT environments. BFL utilizes a novel weighted average time approach to mitigate both straggler effects and communication bottlenecks, ensuring fairness between clients with varying processing speeds through collaboration with a buffer-based server. The performance results, derived from two datasets, show the superiority of BFL compared to state-of-the-art FL methods, demonstrating improved accuracy and convergence speed while enhancing privacy preservation.

Figures (3)

• Figure 1: High-level architecture for privacy-preserving BFL.
• Figure 2: Accuracy comparison of MLP-based algorithm: 5 clients, 10 iterations for Gas pipeline and 4 iterations for WUSTL-IIoT.
• Figure 3: Convergence speed comparison of MLP-based algorithms.