5 citations · 10 across the 3 of their papers we have counts for
3 papers
Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learning
Ngoc Duy Pham, Khoa Tran Phan, Naveen Chilamkurti
Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. Only processed or `smashed' data can be tr…
Split Learning without Local Weight Sharing to Enhance Client-side Data Privacy
Ngoc Duy Pham, Tran Khoa Phan, Alsharif Abuadbba +3
Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. In SL training with multiple clients, the…
Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction
Ngoc Duy Pham, Alsharif Abuadbba, Yansong Gao +2
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…