{
  "id": 18296,
  "url": "https://arxiv.org/abs/2608.09082v1",
  "title": "F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting",
  "summary": "Spatiotemporal prediction on graph-structured data is central to traffic forecasting and environmental monitoring, yet decentralized and heterogeneous data complicate both sequence modeling and collaborative training. We propose F$^2$STNet, a federated forecasting framework that combines truncated graph-Fourier features, a lightweight diagonal state-space temporal encoder, graph convolution, and Fairness-aware Federated Aggregation (FFA). The spectral branch exposes graph-frequency structure, wh",
  "authors": "Jiayi Zhang, Jinfeng Xu, Hewei Wang, Siyuan Cen, Haidong Huang, Yiyao Zhan, Zheyu Chen, Jinjiang You, Ai Jian, Edith C. H. Ngai",
  "category": "research",
  "topics": "bias-fairness,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T03:29:15.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18296",
  "original_url": "https://arxiv.org/abs/2608.09082v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}