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This paper presents a comparative analysis of centralized and federated learning (FL) methods for Internet of Things (IoT) anomaly detection, emphasizing model transparency explainability. We simulate a five-client FL setup using real-world IoT datasets to assess how model interpretation changes between local and global aggregation. Our results reveal that while centralized models capture global feature importance effectively, FL introduces inconsistencies due to data heterogeneity and local overfitting. Analysis shows feature drift across clients, while highlighting local decision logic variations. The findings underline the trade-off between privacy-preserving collaborative learning and model interpretability, which is essential for securing IoT networks.

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Dynamic and Explainable Federated Learning for IoT Anomaly Detection: A Comparative Study with Centralized Machine Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • Alend Jarjis,
  • Yaşar Becerikli

摘要

This paper presents a comparative analysis of centralized and federated learning (FL) methods for Internet of Things (IoT) anomaly detection, emphasizing model transparency explainability. We simulate a five-client FL setup using real-world IoT datasets to assess how model interpretation changes between local and global aggregation. Our results reveal that while centralized models capture global feature importance effectively, FL introduces inconsistencies due to data heterogeneity and local overfitting. Analysis shows feature drift across clients, while highlighting local decision logic variations. The findings underline the trade-off between privacy-preserving collaborative learning and model interpretability, which is essential for securing IoT networks.