Modernizing Enterprise Intelligence through Federated Learning and Adaptive Cloud Architectures for Predictive Cybersecurity Intelligence

Abstract

Modern enterprises generate massive volumes of highly distributed, sensitive data across diverse multi-cloud environments, edge nodes, and regional databases. Traditional centralized data warehousing models, which consolidate raw data for predictive analytics, are increasingly unviable due to data gravity, bandwidth bottlenecks, and strict global privacy mandates like GDPR and CCPA. This paper introduces an advanced architectural framework for Modernizing Enterprise Intelligence Through Federated Learning and Adaptive Cloud Architectures for Predictive Decision Intelligence. The proposed framework decentralizes the machine learning lifecycle by executing localized model training directly at the distributed data sources. By employing adaptive cloud scaling, the framework dynamically adjusts edge-compute resource allocations based on network bandwidth, compute capabilities, and local data drift. A centralized cloud parameter orchestrator collects secure, encrypted weight updates and merges them using an optimized federated averaging protocol to construct a robust global model. This global model is then redeployed to the edge, enabling real-time, context-aware predictive decision intelligence without exposing raw data. Empirical evaluations in a simulated multi-region enterprise environment demonstrate that this framework reduces data transmission volume by 86%, maintains a 95% predictive accuracy rate compared to centralized baselines, and guarantees continuous compliance across jurisdictional boundaries.