Federated Learning for Privacy-Preserving Cybersecurity: Enhancing Data Protection in Decentralized AI Models

Abstract

With the rapid proliferation of connected devices and growing volumes of sensitive data, safeguarding privacy in cybersecurity has become increasingly critical. Federated learning (FL) offers a promising solution by enabling decentralized AI model training without directly sharing data. This paper explores the potential of federated learning in enhancing data protection in cybersecurity applications, focusing on privacy-preserving techniques, secure aggregation, and the challenge of maintaining robust performance amid decentralized data environments. We highlight case studies in threat detection, identity management, and anomaly detection, providing insight into the trade-offs between privacy, security, and efficiency.