FedHyperGraph: A layer-wise personalized federated learning with correlation graphs in hyperbolic space.
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- Record sourced from PubMed, PMID 41442801.
- Also identified by DOI 10.1016/j.neunet.2025.108481.
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Abstract
The goal of Personalized Federated Learning (PFL) is to achieve model personalization in statistically heterogeneous scenarios. However, most existing approaches treat model parameters as a monolithic unit during aggregation. This coarse-grained strategy overlooks the latent correlations among parameters across different clients, which are crucial when clients share similar tasks but possess heterogeneous data. To address this limitation, we propose FedHyperGraph, a graph-guided aggregation framework for layer-wise PFL. FedHyperGraph captures layer-wise knowledge to construct latent correlation graphs in hyperbolic space, which effectively guide the personalized aggregation process. Extensive experiments demonstrate that FedHyperGraph significantly outperforms state-of-the-art baselines, achieving accuracy improvements of up to 42.6 %, 33.1 %, and 7.5 % on graph, computer vision (CV), and natural language processing (NLP) datasets, respectively. Furthermore, FedHyperGraph accelerates convergence by up to 52.6 % on CV tasks and demonstrates superior scalability across varying client scales.