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This review explores federated learning as a solution for developing privacy-preserving security systems against cyber threats.

April 7, 2026Open Access

Federated Learning For Privacy-Preserving Security Systems

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This review explores federated learning as a solution for developing privacy-preserving security systems against cyber threats.
Examined federated learning architectures including horizontal, vertical, and transfer-based systems.
Analyzed applications in intrusion detection, malware analysis, and behavioral monitoring.
Evaluated integration of differential privacy and secure multi-party computation in model updates.
Federated learning mitigates privacy risks associated with centralized machine learning.
Enhanced model robustness through training on diverse datasets.
Addresses challenges such as communication overhead, non-IID data, and vulnerability to attacks.

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Review explores federated learning to enhance security while protecting privacy, suggesting implications for data governance.

Vikram Iyer (2023) studied this question.

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