Developing Predictive Enterprise Intelligence Using Federated Learning with Autonomous Analytics in Cloud Computing

Abstract

In the modern digital economy, enterprises generate vast amounts of highly sensitive, distributed operational data across multi-cloud environments, edge nodes, and regional databases. Traditional centralized predictive intelligence frameworks demand the ingestion of this raw data into a single cloud data warehouse, presenting severe challenges regarding data gravity, bandwidth saturation, and regulatory compliance. To overcome these limitations, this paper presents a novel framework for Developing Predictive Enterprise Intelligence Using Federated Learning with Autonomous Analytics in Cloud Computing. The proposed architecture decentralizes model training by deploying autonomous, self-tuning machine learning models directly to localized enterprise nodes. These localized models process data at the source, transmitting only secure parameter updates back to a central cloud orchestrator. The orchestrator dynamically aggregates these updates using a secure federated averaging protocol to synthesize a globally optimized predictive model. Furthermore, the framework integrates autonomous analytics to automate feature engineering, hyperparameter optimization, and model validation at the edge, eliminating manual operational bottlenecks. Experimental evaluations indicate that this collaborative, privacy-preserving paradigm reduces data transmission overhead by up to 85%, significantly mitigates data privacy risks, and maintains predictive performance comparable to traditional centralized machine learning models.