CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstruction

• URL: http://arxiv.org/abs/2603.12591v1
• Date: Fri, 13 Mar 2026 02:45:00 GMT
• Title: CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstruction
• Abstract summary: Federated learning on heterogeneous edge devices requires personalized compression while preserving aggregation compatibility and stable convergence.<n>We present Curvature-Aware Heterogeneous Federated Pruning (CA-HFP), a practical framework that enables each client perform structured, device-specific pruning guided by a curvature-informed significance score.
• Score: 6.817747681821888
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning on heterogeneous edge devices requires personalized compression while preserving aggregation compatibility and stable convergence. We present Curvature-Aware Heterogeneous Federated Pruning (CA-HFP), a practical framework that enables each client perform structured, device-specific pruning guided by a curvature-informed significance score, and subsequently maps its compact submodel back into a common global parameter space via a lightweight reconstruction. We derive a convergence bound for federated optimization with multiple local SGD steps that explicitly accounts for local computation, data heterogeneity, and pruning-induced perturbations; from which a principled loss-based pruning criterion is derived. Extensive experiments on FMNIST, CIFAR-10, and CIFAR-100 using VGG and ResNet architectures under varying degrees of data heterogeneity demonstrate that CA-HFP preserves model accuracy while significantly reducing per-client computation and communication costs, outperforming standard federated training and existing pruning-based baselines.

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