Adaptive Federated Analytics Frameworks for Distributed Enterprise Data Systems

Abstract

As enterprise data grows across cloud, edge, and geographically distributed environments, traditional centralized analytics face challenges related to privacy, security, scalability, and regulatory compliance. To address these issues, this study proposes an Adaptive Federated Analytics Framework (AFAF) for distributed enterprise data systems. The framework enables collaborative analytics without sharing raw data by incorporating adaptive node selection, dynamic aggregation, privacy-preserving mechanisms, and communication optimization techniques. Unlike conventional federated approaches, AFAF dynamically evaluates node reliability, computational capacity, data quality, and network conditions to improve analytical performance. The framework integrates differential privacy, secure multi-party computation (SMPC), and encrypted aggregation to ensure enterprise-grade security and compliance. Experimental evaluations in hybrid cloud enterprise environments demonstrate improvements in analytical accuracy, aggregation efficiency, communication overhead, convergence stability, scalability, and fault tolerance compared with traditional federated systems. The framework also supports real-time analytics through adaptive participation thresholds and aggregation frequencies, enabling timely insights from distributed data sources. Results indicate that AFAF provides a scalable, secure, and efficient platform for privacy-preserving enterprise intelligence, with future enhancements including AI-driven orchestration, blockchain-based trust management, and autonomous analytics optimization.