2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Sharpness-aware Federated Graph Learning
Ruiyu Li, Peige Zhao, Guangxia Li +3
One of many impediments to applying graph neural networks (GNNs) to large-scale real-world graph data is the challenge of centralized training, which requires aggregating data from…
cs.LG2025
FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices
Dezhong Yao, Yuexin Shi, Tongtong Liu +1
Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The it…
cs.LG2023★ 2 cited
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Minbo Ma, Jilin Hu, Christian S. Jensen +4
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use o…