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FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction — arXiv:2609.20026

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.20026
License (as archived): CC0-1.0
Archived: 2026-10-10T03:21:22.622Z
SHA-256 of the archived text: ad7e61c74b3f1118b48e8f6a2f9a49d42811ac02223b43e206c6a77375b567db
Stable URL of this page: https://metraipolis.com/documento/dc_99eebf403eb6

How to cite

FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction — arXiv:2609.20026 — arXiv (en), https://arxiv.org/abs/2609.20026 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_99eebf403eb6 (Metraipolis archive dc_99eebf403eb6, sha256 ad7e61c74b3f1118b48e8f6a2f9a49d42811ac02223b43e206c6a77375b567db)

BibTeX

@misc{dc99eebf403eb6,
  title = {FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction — arXiv:2609.20026},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.20026},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_99eebf403eb6; sha256 ad7e61c74b3f1118b48e8f6a2f9a49d42811ac02223b43e206c6a77375b567db},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.20026. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to privacy or commercial constraints, motivating federated spatial-temporal approaches. In such federated settings, each client observes traffic over a distinct sensor subgraph with its own spatial topology and temporal dynamics, leading to significant heterogeneity across clients. Existing federated spatial-temporal methods typically rely on model parameter aggregation and provide limited mechanisms for recovering spatial dependencies across client boundaries. This introduces two key limitations. Specifically, parameter aggregation across heterogeneous graph domains tends to dilute client-specific representations, while road network partitioning breaks the propagation of traffic dynamics across client boundaries. To address these challenges, we propose FedeRICo, a federated traffic forecasting framework that combines gradient-level collaboration with boundary-aware residual communication. FedeRICo employs a dual-branch forecasting architecture in which a globally guided branch captures transferable forecasting structure, while a private residual branch preserves client-specific corrections and incorporates boundary residual signals. The global branch is coordinated through gradient alignment across all clients, enabling collaborative optimisation without destructive parameter interference. To recover cross-client spatial dependencies, boundary messages are extracted through a trend-residual decomposition that suppresses periodic structure and communicates only transient spatial-temporal residual signals between physically adjacent clients. Experiments across four real-world traffic forecasting benchmarks demonstrate that FedeRICo consistently outperforms state-of-the-art federated spatial-temporal baselines while maintaining competitive training runtime.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-18 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2609.20026 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T03:04:13.685Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).