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May 31, 2024 · We propose a novel class of HNs, sheaf hypernetworks (SHNs), which combine cellular sheaf theory with HNs to improve parameter sharing for PFL.
May 31, 2024 · We propose a novel class of HNs, sheaf hypernetworks (SHNs), which combine cellular sheaf theory with HNs to improve parameter sharing for PFL.
Jun 27, 2024 · To mitigate these limitations in the context of PFL, we propose a novel class of HNs, sheaf hypernetworks (SHNs), which combine cellular sheaf ...
Since hypernetworks share information across clients, it is shown that pFedHN can generalize better to new clients whose distributions differ from any ...
Jun 2, 2024 · This paper introduces a novel deep learning technique called Sheaf HyperNetworks for personalized federated learning on graph-structured data.
Dec 31, 2023 · Personalized subgraph Federated Learning (FL) is a task that customizes Graph Neural Networks (GNNs) to individual client needs, ...
Jul 9, 2024 · Bao Nguyen presenting his work on "Sheaf HyperNetworks for Personalized Federated Learning". #mobiuk2024. Image. Cambridge University.
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Sheaf HyperNetworks for Personalized Federated Learning. no code yet • 31 May 2024. Graph hypernetworks (GHNs), constructed by combining graph neural networks ...
Sheaf HyperNetworks for Personalized Federated Learning. B Nguyen, L Sani, X Qiu, P Liò, ND Lane. arXiv preprint arXiv:2405.20882, 2024. 1, 2024 ; Worldwide ...
Sep 12, 2024 · We propose a model called FedSheafHN, using enhanced collaboration graph embedding and efficient personalized model parameter generation.