August 11, 2025

Federated Learning for Distributed Network Security and Threat Intelligence: A Privacy-Preserving Paradigm for Scalable Cyber Defense

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Federated learning enhances privacy-preserving network security, enabling robust cyber defense against attacks.

The federated learning framework improves detection accuracy while preserving user privacy, addressing key challenges in network security.
Models were evaluated using benchmark intrusion detection datasets, with results supporting the superiority of federated approaches.
Analysis involved both IID and non-IID data sets to demonstrate the framework's flexibility in various scenarios.
This approach enhances defenses against adversarial attacks, with significant implications for future cybersecurity systems.

Oluwatosin Oladayo ARAMIDE (2025) studied this question.

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