[2603.01739] CA-AFP: Cluster-Aware Adaptive Federated Pruning

Computer Science > Machine Learning arXiv:2603.01739 (cs) [Submitted on 2 Mar 2026] Title:CA-AFP: Cluster-Aware Adaptive Federated Pruning Authors:Om Govind Jha, Harsh Shukla, Haroon R. Lone View a PDF of the paper titled CA-AFP: Cluster-Aware Adaptive Federated Pruning, by Om Govind Jha and 2 other authors View PDF HTML (experimental) Abstract:Federated Learning (FL) faces major challenges in real-world deployments due to statistical heterogeneity across clients and system heterogeneity arising from resource-constrained devices. While clustering-based approaches mitigate statistical heterogeneity and pruning techniques improve memory and communication efficiency, these strategies are typically studied in isolation. We propose CA-AFP, a unified framework that jointly addresses both challenges by performing cluster-specific model pruning. In CA-AFP, clients are first grouped into clusters, and a separate model for each cluster is adaptively pruned during training. The framework introduces two key innovations: (1) a cluster-aware importance scoring mechanism that combines weight magnitude, intra-cluster coherence, and gradient consistency to identify parameters for pruning, and (2) an iterative pruning schedule that progressively removes parameters while enabling model self-healing through weight regrowth. We evaluate CA-AFP on two widely used human activity recognition benchmarks, UCI HAR and WISDM, under natural user-based federated partitions. Experimental results demonst...