GraphPL: Leveraging GNN for Efficient and Robust Modalities Imputation in Patchwork Learning

ArXi:2604.25352v1 Announce Type: new 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.