Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms

--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. Artificial intelligence and deep learning computations are widely used in many areas. Among them, deep learning for medical applications is one of the most remarkable applications, where deep learning algorithms are directly utilized for medical applications, e.g., learning-based abnormality detection in medical imaging, statistical inference for public health, and deep learning based preventive medicine [1], [2]. Medical images are large and sensitive by nature, making scalability and privacy two pressing issues in applying deep learning to the problem at hand.