Architecting Federated Learning with Multi-Cloud Infrastructure for Privacy-Preserving Enterprise Cybersecurity Analytics

Abstract

The increasing frequency and sophistication of cyber threats have compelled enterprises to adopt intelligent security analytics capable of detecting, predicting, and mitigating attacks in real time. However, conventional centralized machine learning approaches require organizations to aggregate sensitive security logs and user data into a single repository, creating significant privacy, regulatory, and operational challenges. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training without exposing raw data, thereby preserving organizational confidentiality while leveraging distributed intelligence. When integrated with multi-cloud infrastructure, federated learning enhances scalability, fault tolerance, computational flexibility, and resilience by utilizing heterogeneous cloud platforms across multiple service providers. This study explores the architectural design of federated learning within a multi-cloud environment for enterprise cybersecurity analytics, emphasizing secure model aggregation, privacy preservation, and cross-organizational collaboration. The research examines existing literature, identifies architectural challenges, and proposes a methodology for implementing privacy-preserving cybersecurity analytics through distributed learning frameworks. Security mechanisms including differential privacy, secure aggregation, encryption, and blockchain-based auditing are considered to strengthen trust and protect model updates from adversarial attacks. The proposed architecture aims to improve threat detection accuracy while maintaining compliance with data protection regulations. The findings contribute to developing scalable, secure, and privacy-aware cybersecurity ecosystems capable of supporting modern enterprises operating across geographically distributed and cloud-enabled environments.