Federated Intelligence for Privacy-Aware Security Across Distributed Cloud Enterprise Networks Using Collaborative Learning

Abstract

The increasing adoption of distributed cloud enterprise networks has intensified cybersecurity challenges involving data privacy, heterogeneous infrastructure, and coordinated threat detection. Conventional centralized security analytics requires organizations to transfer sensitive network telemetry to common repositories, potentially increasing privacy risks, regulatory concerns, and exposure to unauthorized access. Federated learning offers an alternative by enabling multiple enterprise participants to collaboratively train machine learning models while retaining raw data within their respective environments. This research proposes a federated intelligence framework for privacy-aware security across distributed cloud enterprise networks using collaborative learning. The framework integrates local threat detection, federated model aggregation, privacy-preserving parameter exchange, anomaly detection, and adaptive security intelligence. Participating cloud environments independently process network flows, authentication records, application logs, and workload telemetry to identify suspicious activities and generate local model updates. A federated coordination layer combines these updates to improve collective threat detection without routinely centralizing raw security records. Differential privacy, secure aggregation, authenticated communication, and participant validation are considered to strengthen confidentiality and model integrity. The framework also addresses non-identically distributed security data, communication overhead, model poisoning, and evolving attack patterns. Its effectiveness should be evaluated using detection accuracy, precision, recall, false-positive rate, communication cost, convergence time, and privacy-related measurements. The proposed approach provides a foundation for collaborative, scalable, and privacy-aware cybersecurity intelligence across heterogeneous enterprise cloud networks while supporting organizational data sovereignty and responsible security information sharing.