Privacy-Preserving Federated Learning in Cybersecurity Applications

Abstract

The advent of decentralized machine learning paradigms, particularly Federated Learning (FL), has significantly transformed how sensitive data can be harnessed for cybersecurity without violating privacy regulations. Traditional centralized machine learning models often require massive data collection in a single location, which presents risks related to data breaches and unauthorized access. Federated Learning addresses these issues by training models across decentralized devices or servers holding local data samples, without transferring raw data to a central server. However, FL itself faces several privacy threats, such as model inversion, membership inference, and gradient leakage. In this study, we explore the integration of privacy-preserving techniques into FL architectures to enhance cybersecurity systems. We assess the use of differential privacy, secure multiparty computation, and homomorphic encryption in federated learning scenarios applied to threat detection, intrusion prevention, and anomaly analysis. We conduct experiments on cybersecurity datasets using privacy-enhanced FL models and evaluate them in terms of accuracy, communication efficiency, and privacy leakage. The results demonstrate the viability of privacy-preserving FL in practical cybersecurity environments while highlighting challenges and trade-offs.