Securing Cloud Infrastructure using Federated Machine Learning Frameworks

Abstract

With the rapid expansion of cloud computing services, data security and privacy have become pressing challenges in modern digital ecosystems. Traditional centralized machine learning approaches for threat detection and anomaly analysis in cloud environments often require aggregating sensitive data from distributed nodes, increasing the risk of exposure and data breaches. Federated Machine Learning (FML) offers a transformative solution by enabling collaborative model training across multiple cloud nodes without sharing raw data. This paper explores how federated machine learning can be integrated into cloud infrastructure to enhance security, privacy, and resilience. By distributing intelligence across multiple tenants and service layers, federated frameworks enable real-time detection of insider threats, intrusions, and misconfigurations while maintaining compliance with data protection regulations. The discussion further highlights architectural strategies, communication optimization, and the integration of secure aggregation protocols to prevent model poisoning and adversarial attacks. The paper concludes that federated learning not only preserves privacy but also establishes a scalable foundation for proactive, autonomous, and self-healing cloud security systems.