English(EN) Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

Sylvas框架优化联邦持续学习的设备调度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

提出了一种名为Sylvas的新框架，通过优化设备调度来改进联邦持续学习（FCL）。该方法量化了来自分布式边缘设备数据的学习价值，同时考虑了分布价值和标签价值。通过整合这些指标，Sylvas旨在调度具有高学习价值的设备，同时遵守通信和计算资源限制，从而在智能交通和工业监控等应用中实现及时的模型适应。 AI

影响通过优化持续学习的设备调度，提高了分布式AI系统的效率。

排序理由该条目是一篇研究论文，详细介绍了一种用于联邦持续学习的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yuxuan Sun, Yuxuan Bai, Tan Chen, Sheng Zhou, Zhisheng Niu · 2026-09-15 04:00

Sylvas：联邦持续学习中基于设备调度的协同学习价值

arXiv:2609.15763v1 Announce Type: cross Abstract: Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it important for Internet of Things applications such as intelligent transportation, indu…

本文如何被排名

报道来源 [1]

Sylvas：联邦持续学习中基于设备调度的协同学习价值

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