  Although data-driven methods usually have noticeable performance on disease
diagnosis and treatment, they are suspected of leakage of privacy due to
collecting data for model training. Recently, federated learning provides a
secure and trustable alternative to collaboratively train model without any
exchange of medical data among multiple institutes. Therefore, it has draw much
attention due to its natural merit on privacy protection. However, when
heterogenous medical data exists between different hospitals, federated
learning usually has to face with degradation of performance. In the paper, we
propose a new personalized framework of federated learning to handle the
problem. It successfully yields personalized models based on awareness of
similarity between local data, and achieves better tradeoff between
generalization and personalization than existing methods. After that, we
further design a differentially sparse regularizer to improve communication
efficiency during procedure of model training. Additionally, we propose an
effective method to reduce the computational cost, which improves computation
efficiency significantly. Furthermore, we collect 5 real medical datasets,
including 2 public medical image datasets and 3 private multi-center clinical
diagnosis datasets, and evaluate its performance by conducting nodule
classification, tumor segmentation, and clinical risk prediction tasks.
Comparing with 13 existing related methods, the proposed method successfully
achieves the best model performance, and meanwhile up to 60% improvement of
communication efficiency. Source code is public, and can be accessed at:
https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code.
