PFLSE: A Personalized Federated Learning Framework Based on Shannon Entropy Metric for Intrusion Detection in IIoT

Le résumé fourni par la source

Intrusion detection is a crucial method for addressing the security risks of the Industrial Internet of Things (IIoT). However, acquiring substantial and high-quality training data can be challenging for centralized schemes. While federated learning has shown great application prospects as a secure distributed solution, it also encounters the problems of heterogeneous and imbalanced data in real-world production environments. In this article, we propose a personalized federated learning scheme based on Shannon entropy metric (PFLSE), aimed at providing a high-accuracy customized detection model for local organizations. This scheme introduces Shannon entropy into the aggregation mechanism, allowing the edge agent model, which contains richer global information, to carry greater weight in the aggregation process. In the local training process, a two-stage training strategy based on the concept of personalized layer is firstly applied to strengthen the global features and local personalized representations. Secondly, considering the differential balance degree between various edge agent data, a Shannon entropy based dynamic loss function (SDL) is proposed, which combines focal loss and cross-entropy loss, to improve training stability and alleviate the difficulty of training on imbalanced data. Finally, a comprehensive experiment simulating a real-world environment shows that PFLSE exhibits reliable intrusion detection performance across metrics such as accuracy, precision, andF1-Score. Furthermore, it outperforms other methods in the scenarios involving non-independent and identically distributed (non-IID) data.

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