29 citations · 37 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…
GitFL: Adaptive Asynchronous Federated Learning using Version Control
Ming Hu, Zeke Xia, Zhihao Yue +4
As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AI…