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Evidence record 18296 · automatically gathered

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting

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

Record details

Published: 10 August 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness · Environment
Retrieved: 11 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (10 August 2026), “F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting,” evidence record 18296, https://ethics.ai/record/18296 (originally published by arXiv fairness query).

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