Adaptive Federated Learning with Dynamic Resource Allocation for Distributed Big Data Analytics: A Novel Framework for Privacy-Preserving Machine Learning at Scale

The exponential growth of data generation across distributed systems presents unprecedented challenges for traditional machine learning approaches. This paper introduces AFRA-ML (Adaptive Federated Resource Allocation for Machine Learning), a novel framework that combines federated learning with dynamic resource allocation to enable scalable, privacy-preserving machine learning on massive distributed datasets. Our approach addresses three critical limitations in current big data ML systems: computational bottlenecks in centralized processing, privacy concerns in data aggregation, and inefficient resource utilization across heterogeneous computing environments. AFRA-ML employs a hierarchical federated architecture with adaptive model compression, differential privacy mechanisms, and intelligent workload distribution based on real-time resource availability. Experimental results on five large-scale datasets (ranging from 10TB to 500TB) demonstrate that AFRA-ML achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.2 \times$</tex> faster training convergence, 89 % reduction in communication overhead, and maintains model accuracy within 2 % of centralized approaches while ensuring <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex>-differential privacy with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon \leq 0.1$</tex>. The framework successfully scales to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10,000+$</tex> participating nodes with linear scalability characteristics, making it suitable for enterprise-scale deployments across cloud, edge, and hybrid computing environments.