Personalized Subgraph Federated Learning with Sheaf Collaboration

• URL: http://arxiv.org/abs/2508.13642v1
• Date: Tue, 19 Aug 2025 08:52:50 GMT
• Title: Personalized Subgraph Federated Learning with Sheaf Collaboration
• Authors: Wenfei Liang, Yanan Zhao, Rui She, Yiming Li, Wee Peng Tay,
• Abstract summary: FedSheafHN is a novel framework built on a sheaf collaboration mechanism to unify enhanced client descriptors with efficient personalized model generation.<n>Specifically, FedSheafHN embeds each client's local subgraph into a server-constructed collaboration graph.<n>It generates customized client models via a server-optimized hypernetwork.
• Score: 22.825083541211168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph-structured data is prevalent in many applications. In subgraph federated learning (FL), this data is distributed across clients, each with a local subgraph. Personalized subgraph FL aims to develop a customized model for each client to handle diverse data distributions. However, performance variation across clients remains a key issue due to the heterogeneity of local subgraphs. To overcome the challenge, we propose FedSheafHN, a novel framework built on a sheaf collaboration mechanism to unify enhanced client descriptors with efficient personalized model generation. Specifically, FedSheafHN embeds each client's local subgraph into a server-constructed collaboration graph by leveraging graph-level embeddings and employing sheaf diffusion within the collaboration graph to enrich client representations. Subsequently, FedSheafHN generates customized client models via a server-optimized hypernetwork. Empirical evaluations demonstrate that FedSheafHN outperforms existing personalized subgraph FL methods on various graph datasets. Additionally, it exhibits fast model convergence and effectively generalizes to new clients.

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