Chung-ju Huang, Huiqiang Zhao, Yuanpeng He, Lijian Li, Wenpin Jiao, Zhi Jin, Peixuan Chen, Leye Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Cross-hospital collaboration has the potential to mitigate disparities in medical resources across different regions. However, strict privacy regulations prohibit the direct sharing of sensitive patient information between hospitals. Vertical Federated Learning (VFL) provides a novel privacy-preserving machine learning paradigm designed to maximizes data utility across multiple hospitals. Nevertheless, traditional VFL methods primarily benefit patients with overlapping data, leaving non-overlapping patients without guaranteed improvements in distributed healthcare prediction services. While some existing knowledge transfer techniques attempt to improve prediction performance for non-overlapping patients, they fail to adequately address scenarios where overlapping and non-overlapping patients originate from different domains, resulting in challenges such as feature and label heterogeneity. To address these issues, we propose UniTrans, a unified vertical federated knowledge transfer framework for edge healthcare collaboration. Our framework consists of three key steps. First, we extract the federated representation of overlapping patients by employing an effective vertical federated representation learning method to model multi-party joint features online. Next, each hospital learns a local knowledge transfer module offline, enabling the domain-adaptive transfer of knowledge from the federated representation of overlapping patients to the enriched representation of local non-overlapping patients. Finally, hospitals utilize these enriched local representations to enhance performance across various downstream medical prediction tasks. Extensive experiments on real-world medical datasets demonstrate the effectiveness and scalability of UniTrans in both intra-domain and cross-domain knowledge transfer. The code of UniTrans is available athttps://github.com/Chung-ju/Unitrans.

Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients’ highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties’ shared samples, which strongly restricts its application scope. In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. Specifically, our framework includes three steps. First, shared samples’ federated representations are extracted by collaboratively modeling multi-parties’ joint features with current efficient vertical federated representation learning methods. Second, for each hospital, we learn a local-representation-distilled module, which can transfer the knowledge from shared samples’ federated representations to enrich local samples’ representations. Finally, each hospital can leverage local samples’ representations enriched by the distillation module to boost arbitrary downstream machine learning tasks. The experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework.