33 citations · 47 across the 5 of their papers we have counts for
5 papers
Model Extraction Attacks on Split Federated Learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen +4
Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients' data but turns out to be highly vulner…
Profile-Guided Parallel Task Extraction and Execution for Domain Specific Heterogeneous SoC
Liangliang Chang, Joshua Mack, Benjamin Willis +4
In this study, we introduce a methodology for automatically transforming user applications in the radar and communication domain written in C/C++ based on dynamic profiling to a pa…
An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data
Jingtao Li, Jian Zhou, Yan Xiong +2
Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme. Farthest Point Sampling (FPS), which iteratively samples the farthest poi…
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
Jingtao Li, Adnan Siraj Rakin, Xing Chen +3
This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activat…
Communication and Computation Reduction for Split Learning using Asynchronous Training
Xing Chen, Jingtao Li, Chaitali Chakrabarti
Split learning is a promising privacy-preserving distributed learning scheme that has low computation requirement at the edge device but has the disadvantage of high communication…