Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare

• URL: http://arxiv.org/abs/2012.12591v1
• Date: Wed, 23 Dec 2020 10:45:52 GMT
• Title: Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
• Authors: Manish Gawali, Arvind C S, Shriya Suryavanshi, Harshit Madaan, Ashrika Gaikwad, Bhanu Prakash KN, Viraj Kulkarni, Aniruddha Pant
• Abstract summary: 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. We propose a novel distributed learning architecture called SplitFedv3, which performs better than split learning and SplitFedv2 in our experiments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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