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Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction. (arXiv:2206.04864v1 [cs.LG])
June 13, 2022, 1:20 a.m. | Ngoc Duy Pham, Alsharif Abuadbba, Yansong Gao, Tran Khoa Phan, Naveen Chilamkurti
cs.CR updates on arXiv.org arxiv.org
Split learning (SL) enables data privacy preservation by allowing clients to
collaboratively train a deep learning model with the server without sharing raw
data. However, SL still has limitations such as potential data privacy leakage
and high computation at clients. In this study, we propose to binarize the SL
local layers for faster computation (up to 17.5 times less forward-propagation
time in both training and inference phases on mobile devices) and reduced
memory usage (up to 32 times less memory …
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