activity
20222025
most citedFedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation

30 citations · 145 across the 22 of their papers we have counts for

collaborators
Showing 2023Show all

7 papers · 1 filter

cs.LG2023★ 4 cited

AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems

Ruixuan Liu, Ming Hu, Zeke Xia +5

Federated Learning (FL) enables collaborative learning of large-scale distributed clients without data sharing. However, due to the disparity of computing resources among massive m…

cs.LG2023★ 25 cited

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★ 4 cited

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…

cs.LG2023★ 2 cited

FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning

Yanxin Yang, Ming Hu, Xiaofei Xie +4

As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the…

cs.LG2023★ 27 cited

Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination

Ming Hu, Zhihao Yue, Xiaofei Xie +6

Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Fede…

cs.LG2023★ 10 cited

Towards Interpretable Federated Learning

Anran Li, Rui Liu, Ming Hu +4

Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespre…

cs.LG2023★ 1 cited

CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning

Pengyu Zhang, Yingbo Zhou, Ming Hu +2

Federated learning (FL) has been proposed to enable distributed learning on Artificial Intelligence Internet of Things (AIoT) devices with guarantees of high-level data privacy. Si…