  Depression is one of the most common mental illness problems, and the
symptoms shown by patients are not consistent, making it difficult to diagnose
in the process of clinical practice and pathological research. Although
researchers hope that artificial intelligence can contribute to the diagnosis
and treatment of depression, the traditional centralized machine learning needs
to aggregate patient data, and the data privacy of patients with mental illness
needs to be strictly confidential, which hinders machine learning algorithms
clinical application. To solve the problem of privacy of the medical history of
patients with depression, we implement federated learning to analyze and
diagnose depression. First, we propose a general multi-view federated learning
framework using multi-source data, which can extend any traditional machine
learning model to support federated learning across different institutions or
parties. Secondly, we adopt late fusion methods to solve the problem of
inconsistent time series of multi-view data. Finally, we compare the federated
framework with other cooperative learning frameworks in performance and discuss
the related results.
