3 citations · 9 across the 8 of their papers we have counts for
6 papers · 1 filter
Commute Graph Neural Networks
Wei Zhuo, Han Yu, Guang Tan +1
Graph Neural Networks (GNNs) have shown remarkable success in learning from graph-structured data. However, their application to directed graphs (digraphs) presents unique challeng…
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
Hongyi Peng, Han Yu, Xiaoli Tang +1
Federated learning (FL) enables collaborative machine learning across distributed data owners, but data heterogeneity poses a challenge for model calibration. While prior work focu…
Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning
Xiaoli Tang, Han Yu, Xiaoxiao Li
Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners (DOs) to join FL through economic means. While many exis…
Intelligent Agents for Auction-based Federated Learning: A Survey
Xiaoli Tang, Han Yu, Xiaoxiao Li +1
Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data o…
Hierarchical Federated Learning Incentivization for Gas Usage Estimation
Has Sun, Xiaoli Tang, Chengyi Yang +5
Accurately estimating gas usage is essential for the efficient functioning of gas distribution networks and saving operational costs. Traditional methods rely on centralized data p…
Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning
Xiaoli Tang, Han Yu
Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners to join FL through economic means. Existing works assume…