GraphPL: Leveraging GNN for Efficient and Robust Modalities Imputation in Patchwork Learning
Current research on distributed multi-modal learning typically assumes that clients can access complete information across all modalities, which may not hold in practice. In this paper, we explore patchwork learning, in which the modalities available to different clients vary, and the objective is to impute the missing modalities for each client in an unsupervised manner. Existing methods are shown not to fully utilize the modality information as they tend to rely on only a subset of the observed modalities. To address this issue, we propose GraphPL, which combines graph neural networks with patchwork learning to flexibly integrate all observed modalities and remains robust with noisy inp...
| Authors | Xingjian Hu, Zuoyu Yan, Jianhua Zhu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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