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CoCoFL: Continual Computing for Federated Learning over Intermittent Satellite-Ground Links — arXiv:2609.05997

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05997
License (as archived): CC0-1.0
Archived: 2026-10-16T13:07:08.998Z
SHA-256 of the archived text: 70172dc001332b67727980433623ca7ffb2468e0018708d0b5b695050255ced2
Stable URL of this page: https://metraipolis.com/documento/dc_ad2ec6dc620c

How to cite

CoCoFL: Continual Computing for Federated Learning over Intermittent Satellite-Ground Links — arXiv:2609.05997 — arXiv (en), https://arxiv.org/abs/2609.05997 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ad2ec6dc620c (Metraipolis archive dc_ad2ec6dc620c, sha256 70172dc001332b67727980433623ca7ffb2468e0018708d0b5b695050255ced2)

BibTeX

@misc{dcad2ec6dc620c,
  title = {CoCoFL: Continual Computing for Federated Learning over Intermittent Satellite-Ground Links — arXiv:2609.05997},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05997},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ad2ec6dc620c; sha256 70172dc001332b67727980433623ca7ffb2468e0018708d0b5b695050255ced2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05997. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Low earth orbit (LEO) satellite constellations enable geographically distributed ground devices to collaboratively train a global model via federated learning (FL) without sharing raw data, with applications in environmental monitoring and disaster prediction. However, in satellite-assisted FL scenarios, intermittent satellite-ground links allow only a subset of devices to participate in global aggregation within each visibility window, leaving unscheduled devices idle and their local computational and data resources underutilized. Under partial device participation, data heterogeneity among devices may bias the global model toward certain devices, thereby deteriorating learning performance. In this regard, we propose a continual computing based federated learning framework, referred to as CoCoFL, in which scheduled devices participate in the global model aggregation, while unscheduled devices continue updating their local models taking into account model staleness. Guided by the convergence analysis of CoCoFL and subject to visible-window-related time constraints, we jointly optimize the device scheduling and the number of local epochs for scheduled and unscheduled devices. Experimental results demonstrate that CoCoFL achieves faster convergence, lower training loss, and higher test accuracy compared with baselines.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-09 in cs.DC).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2609.05997 — 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 2026-10-16T13:02:21.551Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).