mlsanityData to 14 Oct 2026

Personalized Subgraph Federated Learning with Sheaf Collaboration

arXiv 2508.13642 ↗First posted 19 Aug 2025cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 35 of its 48 references that are in the map. First posted on arXiv 19 Aug 2025.

Builds on

35 of 48 references.

References · 35

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23 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Representation-Aligned Multi-Scale Personalization for Federated Learningcited by 0