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Federated Learning (FL) offers a ground breaking approach to distributed machine learning, enabling collaborative model training while safeguarding data privacy in interconnected networks. This discussion explores FL's application in anomaly detection, emphasizing its capacity to enhance security and efficiency. By training models locally on decentralized devices, FL mitigates privacy concerns and allows for real-time adaptation. The comprehensive methodology encompasses data collection, preprocessing, model selection, and robust communication. The comparative analysis highlights FL’s potential and significant works in the field. FL’s rising significance is underscored by its relevance in addressing modern security challenges. Its scope covers privacy preservation, adaptive learning, and resource optimization.

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Federated Learning Approaches for Anomaly Detection in Distributed Network Environments

  • 会议论文

  • 发表日期：

    2025年

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  • Pokuri Deepika,
  • T. Jyotsna,
  • Ganapavarapu Surekha,
  • T. Pratyusha,
  • R. Usha,
  • G. Lakshmi Praveena

摘要

Federated Learning (FL) offers a ground breaking approach to distributed machine learning, enabling collaborative model training while safeguarding data privacy in interconnected networks. This discussion explores FL's application in anomaly detection, emphasizing its capacity to enhance security and efficiency. By training models locally on decentralized devices, FL mitigates privacy concerns and allows for real-time adaptation. The comprehensive methodology encompasses data collection, preprocessing, model selection, and robust communication. The comparative analysis highlights FL’s potential and significant works in the field. FL’s rising significance is underscored by its relevance in addressing modern security challenges. Its scope covers privacy preservation, adaptive learning, and resource optimization.