25 citations · 35 across the 12 of their papers we have counts for
12 papers
Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing
Zhufeng Lu, Chentao Jia, Ming Hu +2
As a promising privacy-aware collaborative model training paradigm, Federated Learning (FL) is becoming popular in the design of distributed recommender systems. However, Federated…
VulStamp: Vulnerability Assessment using Large Language Model
Hao Shen, Ming Hu, Xiaofei Xie +2
Although modern vulnerability detection tools enable developers to efficiently identify numerous security flaws, indiscriminate remediation efforts often lead to superfluous develo…
Intention is All You Need: Refining Your Code from Your Intention
Qi Guo, Xiaofei Xie, Shangqing Liu +3
Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in siz…
NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing
Fei Gao, Ming Hu, Zhiyu Xie +4
With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud Computing (JCC) is promising t…
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…
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…
MAVIN: Multi-Action Video Generation with Diffusion Models via Transition Video Infilling
Bowen Zhang, Xiaofei Xie, Haotian Lu +3
Diffusion-based video generation has achieved significant progress, yet generating multiple actions that occur sequentially remains a formidable task. Directly generating a video w…
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…
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…
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…