Partially shared federated multiview learning

With the diversification of data representations, the field of multiview learning has attracted the attention of many researchers. However, multiview data is stored in different devices in practical applications, and existing multiview learning methods fail to apply directly. Moreover, the data transmission process is often accompanied by the risk of privacy leakage. Therefore, this paper proposes a multiview learning method based on a vertical federated learning framework called partially shared federated multiview learning method (PSFedML) to handle this issue. Our proposed method uses a partial sharing approach that can simultaneously utilize consistent and complementary information between views to learn latent representations. In addition, the transmission of common latent representations between local clients and the central server avoids the risk of privacy leakage. We also give a corresponding iterative optimization algorithm. Experiments on real-world datasets demonstrate the efficiency and superiority of PSFedML. •The learned latent factor reflects the consistent and complementary information among the different views.•A novel multiview learning method based on the vertical federated framework.•Proposed ADMM based optimization algorithm under the vertical federation framework.