论文标题
保护地理分布的医疗大数据平台的隐私学习深度学习计算
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms
论文作者
论文摘要
本文提出了一个分布式的深度学习框架，用于保护隐私的医疗数据培训。为了避免患者在医疗平台中的数据泄漏，深度学习框架中的隐藏层是分开的，并且将第一层保存在平台中，而其他图层则将其保存在集中式服务器中。而将原始患者的数据保留在本地平台中，维持其隐私，利用服务器进行后续层，可以通过在培训期间使用每个平台中的所有数据来改善学习性能。
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.
