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.

翻译：抑郁症是最常见的精神疾病问题之一,患者所表现出的症状并不一致,因此在临床实践和病理研究过程中难以诊断。 尽管研究人员希望人工智能有助于诊断和治疗抑郁症,但传统的中央机器学习需要集中收集患者数据,精神病患者的数据隐私需要严格保密,这妨碍了机器学习算法的临床应用。为了解决抑郁患者医疗史的隐私问题,我们采用了联邦化学习来分析和诊断抑郁症。首先,我们建议采用多源数据,将任何传统的机器学习模式推广到支持不同机构或政党的联邦学习。第二,我们采用迟聚方法来解决多视图数据时间序列不一致的问题。最后,我们比较了联邦化框架与其他合作学习框架的绩效,并讨论相关结果。