Adaptive federated clustering for uncertainty-aware learning on decentralized big data platforms.

ABSTRACT: Federated learning (FL) struggles with scalability in decentralized big data platforms due to data heterogeneity, communication bottlenecks, and computational inefficiencies. We propose Adaptive Federated Clustering (AFC), a novel framework that addresses these challenges through three key innovations: (1) adaptive client selection based on computational capacity and data relevance, (2) hierarchical aggregation organizing clients into clusters for localized updates, and (3) sparsity- and quantization-based model compression. Experiments on CIFAR-10, CIFAR-100, Fashion-MNIST, and MIMIC-III demonstrate AFC achieves 4.3% higher accuracy than FedAvg, 49% lower communication cost, and 35% faster convergence. Under backdoor attacks, AFC shows only 2.8% accuracy degradation versus 7% for FedAvg.

SUBMITTER: Alhazmi MH 

PROVIDER: S-EPMC12668549 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature