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This chapter explores how Federated Learning (FL) addresses the challenges of data privacy and security in cybersecurity applications. Traditional machine learning approaches often require centralized data aggregation, exposing sensitive information to potential risks. FL, as a decentralized paradigm, enables multiple clients to collaboratively train models without sharing raw data, preserving privacy and security. This chapter explores the integration of machine learning into cybersecurity, highlighting advancements in detection and response mechanisms. It further investigates the limitations of traditional methods and presents intelligent systems that use FL to enhance cybersecurity resilience and effectiveness. Through this discussion, the chapter underscores FL’s remarkable potential in creating secure and robust cybersecurity frameworks.

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Cyber Security Intelligent Systems Based on Federated Learning

  • 章节

  • 发表日期：

    2025年

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  • Hamed Tabrizchi,
  • Ali Aghasi

摘要

This chapter explores how Federated Learning (FL) addresses the challenges of data privacy and security in cybersecurity applications. Traditional machine learning approaches often require centralized data aggregation, exposing sensitive information to potential risks. FL, as a decentralized paradigm, enables multiple clients to collaboratively train models without sharing raw data, preserving privacy and security. This chapter explores the integration of machine learning into cybersecurity, highlighting advancements in detection and response mechanisms. It further investigates the limitations of traditional methods and presents intelligent systems that use FL to enhance cybersecurity resilience and effectiveness. Through this discussion, the chapter underscores FL’s remarkable potential in creating secure and robust cybersecurity frameworks.