This paper proposes a distributed deep learning framework for
privacy-preserving medical data training. In order to avoid patients' data
leakage in medical platforms, the hidden layers in the deep learning framework
are separated and where the first layer is kept in platform and others layers
are kept in a centralized server. Whereas keeping the original patients' data
in local platforms maintain their privacy, utilizing the server for subsequent
layers improves learning performance by using all data from each platform
during training.Comment: 2019 IEEE/IFIP International Conference on Dependable Systems and
Networks Supplementa