Federated semi-supervised representation augmentation with cross-institutional knowledge transfer for healthcare collaboration

In the healthcare field, cross-institutional collaboration can fasten medical research progress. Vertical federated learning (VFL) addresses data heterogeneity across multiple medical institutions while ensuring medical data privacy, thereby enhancing the accuracy of disease diagnoses and treatments. However, traditional VFL only benefits from aligned samples, thereby limiting its applicability due to constrained sample sizes, and a large amount of non-aligned data remains untapped, resulting in wasted data. To exert full leverage on the value of all data obtained from medical institutions, this paper proposes a federated healthcare collaborative framework based on semi-supervised representation augmentation mechanism with cross-institutional knowledge transfer (CrossKT-FRA). Specifically, the developed method comprises three steps. First, the federated representations of shared data (aligned data) among medical institutions are extracted through efficient vertical federated representation learning (FRL) methods. Second, the federated knowledge contained in federated representations and potential labels derived through recurrent learning assist local shared data representations in performing supervised augmented learning. Finally, the federated knowledge is transferred indirectly from the representation augmentation module for shared data to the unsupervised representation augmentation module for local private data (non-aligned data). The experimental results show the effectiveness of the proposed knowledge transfer mechanism, whether applied independently or used to enhance VFL on medical datasets. Our findings contribute to a deeper theoretical understanding of VFL, further facilitating the utilization of high-value medical data. By promoting cross-institutional and cross-disciplinary collaboration in healthcare data sharing, our study enhances the quality efficiency of medical services, thereby accelerating the development of interdisciplinary medical research. Code is available at https://github.com/LieLieLieLieLie/CrossKT-FRA.