FedSheafHN: Personalized Federated Learning on Graph-structured Data

• URL: http://arxiv.org/abs/2405.16056v3
• Date: Fri, 31 May 2024 11:44:39 GMT
• Title: FedSheafHN: Personalized Federated Learning on Graph-structured Data
• Authors: Wenfei Liang, Yanan Zhao, Rui She, Yiming Li, Wee Peng Tay,
• Abstract summary: We propose a model called FedSheafHN, which embeds each client's local subgraph into a server-constructed collaboration graph. Our model improves the integration and interpretation of complex client characteristics. It also has fast model convergence and effective new clients generalization.
• Score: 22.825083541211168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized subgraph Federated Learning (FL) is a task that customizes Graph Neural Networks (GNNs) to individual client needs, accommodating diverse data distributions. However, applying hypernetworks in FL, while aiming to facilitate model personalization, often encounters challenges due to inadequate representation of client-specific characteristics. To overcome these limitations, we propose a model called FedSheafHN, using enhanced collaboration graph embedding and efficient personalized model parameter generation. Specifically, our model embeds each client's local subgraph into a server-constructed collaboration graph. We utilize sheaf diffusion in the collaboration graph to learn client representations. Our model improves the integration and interpretation of complex client characteristics. Furthermore, our model ensures the generation of personalized models through advanced hypernetworks optimized for parallel operations across clients. Empirical evaluations demonstrate that FedSheafHN outperforms existing methods in most scenarios, in terms of client model performance on various graph-structured datasets. It also has fast model convergence and effective new clients generalization.

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