Federated learning for cross-cloud observability: Privacy-preserving model aggregation across distributed cloud platforms

This article presents a comprehensive framework for implementing privacy-preserving cross-cloud monitoring using federated learning techniques. As organizations increasingly adopt multi-cloud strategies, maintaining unified observability without violating data sovereignty or regulatory requirements becomes challenging. The innovative system employs federated learning architecture to develop detection models across decentralized, encrypted transaction records, exchanging only model parameter updates between segregated cloud environments while preserving data locality and privacy. The architecture incorporates federated graph neural networks to discover hidden dependencies across cloud boundaries, secure aggregation through homomorphic encryption and secure multi-party computation, and differential privacy safeguards. Through case studies spanning defense, financial services, and healthcare sectors, Article demonstrates significant improvements in incident detection capability, reduction in false positives, and accelerated mean time to resolution while maintaining strict compliance with data protection regulations. The results establish federated learning as a viable solution for achieving cross-cloud observability without compromising sensitive operational data.