Building Autonomous Enterprise Intelligence through Federated Learning Scalable Cloud Data Engineering Frameworks and Cyber Resilience

Abstract

Modern multi-tenant enterprises generate immense, heterogeneous datasets across geographically siloed cloud regions, edge infrastructures, and independent business units. Traditional data engineering paradigms force the collection and centralization of this information into a single cloud data warehouse or lakehouse to build predictive analytics models. This centralized architecture is increasingly untenable due to strict cross-border data residency compliance rules, escalating data egress costs, and the physical constraints of data gravity. To resolve these operational challenges, this paper presents a holistic architectural blueprint for Building Autonomous Enterprise Intelligence through Federated Learning and Scalable Cloud Data Engineering Frameworks. The proposed framework orchestrates a decoupled data ecosystem that reverses traditional Extract-Transform-Load (ETL) paradigms. Instead of moving raw data across networks, the system leverages scalable cloud data pipelines to preprocess, validate, and clean transactional data natively within localized cloud data lakes.  Autonomous federated learning nodes then train advanced machine learning architectures locally, executing parameter optimizations directly at the data source. A centralized cloud parameter orchestrator manages secure model updates, dynamically aggregating local weights using an optimized, privacy-preserving federated averaging protocol to synthesize a globally intelligent model.  Empirical testing within a simulated multi-region enterprise cloud infrastructure proves that this framework guarantees high-fidelity predictive performance, minimizes network egress traffic by up to 88%, and ensures complete compliance with global privacy standards.