FedMASA: Federated Minority-Aware Selection and Aggregation for Non-IID IIoT Intrusion Detection.

Abstract

The heterogeneity of data across Industrial Internet of Things (IIoT) devices poses significant challenges to federated learning-based intrusion detection systems, where the Non-IID data distribution leads to poor detection performance, particularly on rare attack classes. To address these issues, this letter proposes FedMASA, a deep reinforcement learning-assisted federated learning framework with minority-aware selection and aggregation for Non-IID IIoT intrusion detection. Specifically, we formulate client selection as a Markov Decision Process and employ Deep Deterministic Policy Gradient to dynamically select optimal clients based on real-time state observations. A minority-aware aggregation mechanism with class-balanced weighting is designed to amplify the influence of scarce attack classes while penalizing high-latency clients. Extensive experiments on the Edge-IIoTset and ToN-IoT datasets demonstrate that FedMASA consistently surpasses the standard FedAvg baseline across all Non-IID settings. In the most challenging $\alpha=0.1$ scenario, FedMASA outperforms FedAvg by 2.96\% in accuracy and 8.87\% in Macro-F1 on Edge-IIoTset, and by 4.24\% and 10.34\% on ToN-IoT, respectively.