Federated AI Observability in Multi-Cloud Microservices: A Secure and Scalable Federated Learning Perspective

As artificial intelligence (AI) becomes integral to microservices deployed across multi-cloud environments, ensuring secure and scalable observability is critical. Traditional centralized observability methods often fail to address the privacy, compliance, and performance challenges inherent to distributed AI systems. This paper presents a federated learning–based framework for AI observability that preserves data privacy and scalability across heterogeneous cloud platforms. The proposed framework decentralizes telemetry collection and analysis by integrating local observability agents with secure federated aggregation, while maintaining interoperability with modern DevOps pipelines. We evaluate the architecture through case studies in retail, healthcare, and finance sectors, demonstrating improvements in anomaly detection, regulatory compliance, and operational efficiency. Additionally, the paper examines ethical considerations such as data privacy, fairness, and transparency, and outlines future directions including edge observability, privacy-enhanced computation, and automated governance. This research provides a foundational strategy for building trustworthy and efficient observability systems tailored to AI-powered microservices within complex multi-cloud ecosystems. Traditional observability methods struggle with privacy and performance in AI-powered multi-cloud microservices. We propose a federated learning–based framework that enables decentralized telemetry monitoring while ensuring compliance and scalability. Our evaluation across healthcare, finance, and retail shows improvements in anomaly detection latency (25%), fraud detection accuracy (18%), and GDPR/HIPAA alignment. This work lays the groundwork for trustworthy and efficient AI observability in complex cloud-native ecosystems.