30 citations · 145 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation
Jiawen Weng, Zeke Xia, Ran Li +2
Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference…
FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios
Zekai Chen, Chentao Jia, Ming Hu +3
Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable…
KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting
Zeke Xia, Ming Hu, Dengke Yan +4
Although Split Federated Learning (SFL) is good at enabling knowledge sharing among resource-constrained clients, it suffers from the problem of low training accuracy due to the ne…
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance
Zeke Xia, Ming Hu, Dengke Yan +5
Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficien…
MIP: CLIP-based Image Reconstruction from PEFT Gradients
Peiheng Zhou, Ming Hu, Xiaofei Xie +3
Contrastive Language-Image Pre-training (CLIP) model, as an effective pre-trained multimodal neural network, has been widely used in distributed machine learning tasks, especially…
Personalized Federated Instruction Tuning via Neural Architecture Search
Pengyu Zhang, Yingbo Zhou, Ming Hu +3
Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data. However, it still…