Author: Gawali, Manish; ArvindC, S; Suryavanshi, Shriya; Madaan, Harshit; Gaikwad, Ashrika; BhanuPrakash, KN; Kulkarni, Viraj; Pant, Aniruddha
Title: Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare Cord-id: c5ov3b1x Document date: 2020_12_23
ID: c5ov3b1x
Snippet: 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 experi
Document: 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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