1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2025
Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penalty
Ziruo Hao, Zhenhua Cui, Tao Yang +3
This paper provides an invariant federated learning system for resource-constrained edge intelligence. This framework can mitigate the impact of heterogeneity and asynchrony via ex…
cs.LG2025
The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence
Ziruo Hao, Zhenhua Cui, Tao Yang +3
Federated learning (FL) has provided a new methodology for coordinating a group of clients to train a machine learning model collaboratively, bringing an efficient paradigm in edge…
cs.CV2021★ 1 cited
Cross-domain Trajectory Prediction with CTP-Net
Pingxuan Huang, Zhenhua Cui, Jing Li +3
Most pedestrian trajectory prediction methods rely on a huge amount of trajectories annotation, which is time-consuming and expensive. Moreover, a well-trained model may not effect…