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cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
DeepAFL: Deep Analytic Federated Learning
Jianheng Tang, Yajiang Huang, Kejia Fan +8
Federated Learning (FL) is a popular distributed learning paradigm to break down data silo. Traditional FL approaches largely rely on gradient-based updates, facing significant iss…