论文标题
比较医疗保健中保护隐私的分布深度学习方法
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
论文作者
论文摘要
在本文中，我们比较了三种隐私的分布式学习技术：联合学习，分裂学习和分裂。我们使用这些技术来开发二进制分类模型来检测胸部X射线的结核病，并在分类性能，沟通和计算成本以及培训时间方面对其进行比较。我们提出了一种新颖的分布式学习体系结构，称为SplitFedV3，它在我们的实验中的性能要比Split Learning和SplitFEDV2更好。我们还提出了替代小批量培训，这是一种用于分裂学习的新培训技术，其性能比替代客户培训更好，客户轮流训练模型。
In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed. We use these techniques to develop binary classification models for detecting tuberculosis from chest X-rays and compare them in terms of classification performance, communication and computational costs, and training time. We propose a novel distributed learning architecture called SplitFedv3, which performs better than split learning and SplitFedv2 in our experiments. We also propose alternate mini-batch training, a new training technique for split learning, that performs better than alternate client training, where clients take turns to train a model.
