25 citations · 35 across the 12 of their papers we have counts for
7 papers · 1 filter
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…