3 papers
cs.SE2024
An Empirical Study of Vulnerability Detection using Federated Learning
Peiheng Zhou, Ming Hu, Xingrun Quan +6
Although Deep Learning (DL) methods becoming increasingly popular in vulnerability detection, their performance is seriously limited by insufficient training data. This is mainly b…
cs.LG2023
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
Chentao Jia, Ming Hu, Zekai Chen +4
Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classifica…
cs.LG2023
Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning
Dengke Yan, Ming Hu, Zeke Xia +4
Due to its advantages in resource constraint scenarios, Split Federated Learning (SFL) is promising in AIoT systems. However, due to data heterogeneity and stragglers, SFL suffers…