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

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 (FL) 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 first applied to strengthen the global features and local personalized representations. Second, considering the differential balance degree between various edge agent data, a Shannon entropy-based dynamic loss (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, and $F1$ -Score. Furthermore, it outperforms other methods in scenarios involving nonindependent and identically distributed (non-IID) data.

• Publication year

  2026

• Venue

  IEEE Internet of Things Journal

• Publication date

  2026-01-15

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/JIOT.2025.3631089
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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