Privacy-Aware Federated Learning For Distributed Cyber Defense

The increasing use of cloud computing, Internet of Things (IoT) systems, and distributed enterprise networks has significantly increased cybersecurity risks in modern digital environments. Traditional intrusion detection systems often depend on centralized architectures and signature-based approaches that fail to identify evolving cyber threats effectively. Machine learning techniques have improved threat detection capabilities by enabling automated analysis of network traffic and anomaly detection. However, centralized machine learning models require the collection of sensitive data into a single server, creating concerns related to privacy, scalability, and security. Federated learning has emerged as a decentralized solution that allows collaborative model training without sharing raw data. This paper proposes a federated machine learning framework for privacy-preserving cyber threat detection in distributed network environments. The framework integrates privacy-preserving mechanisms, secure aggregation, and scalable deep learning models to improve intrusion detection performance while maintaining data confidentiality. Experimental analysis demonstrates that the proposed federated approach achieves high detection accuracy, reduced communication overhead, and enhanced privacy compared to centralized learning systems.