FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology

arXiv:2402.15858v1 Announce Type: new
Abstract: The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics. However, existing multimodal learning approaches necessitate access to users' raw data, posing substantial privacy risks. While Federated Learning (FL) serves as a privacy-preserving alternative, it falls short in addressing the challenges posed by heterogeneous (yet possibly overlapped) modalities data across various hospitals. To bridge this gap, we propose a Federated Multi-Modal (FedMM) learning framework that federatedly trains multiple single-modal feature extractors …

abstract arxiv challenges computational cs.cv cs.dc data diagnostics federated learning fusion information modal multi-modal multimodal multimodal learning pathology privacy raw risks type