5 papers · 1 filter
F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting
Jiayi Zhang, Jinfeng Xu, Hewei Wang +7
Spatiotemporal prediction on graph-structured data is central to traffic forecasting and environmental monitoring, yet decentralized and heterogeneous data complicate both sequence…
DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification
Jian Chen, Yuzhu Hu, Xiaoyan Yuan +6
Irregular Medical Time Series play a critical role in the clinical domain to better understand the patient's condition. However, inherent irregularity arising from heterogeneous sa…
Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning
Jinfeng Xu, Zheyu Chen, Shuo Yang +4
Early graph prompt tuning approaches relied on task-specific designs for Graph Neural Networks (GNNs), limiting their adaptability across diverse pre-training strategies. In contra…
Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Multiple clustering aims to discover diverse latent structures from different perspectives, yet existing methods generate exhaustive clusterings without discerning user interest, n…
TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction
Weijie Liu, Ziwei Zhan, Carlee Joe-Wong +5
Non-independent and identically distributed (Non-IID) data across edge clients have long posed significant challenges to federated learning (FL) training in edge computing environm…