English(EN) Federated Learning for Cross-Regional Epidemic Forecasting with Asynchronous Outbreaks

联邦学习提升跨区域新冠疫情预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一个联邦学习框架，以改进不同区域的新冠疫情预测，即使在爆发不同步的情况下。通过使用来自广东、澳门和葡萄牙的数据训练长短期记忆网络，该联邦模型在保持数据隐私的同时，实现了与集中式训练相当的预测准确性。研究表明，利用一个区域早期爆发的传播动态可以增强对经历较晚爆发的区域的预测。 AI

影响联邦学习提供了一种保护隐私的协作式人工智能模型训练方法，可能适用于其他跨区域数据挑战。

排序理由该集群包含一篇学术论文，详细介绍了联邦学习在流行病预测方面的新应用。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了联邦学习在流行病预测方面的新应用。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Ruibin Liu, Francisco Fernandes, Xuanfeng Li, Zige Liu, Andr\'e Salgado, Chitin Hon, Zifeng Yang, Arlindo Oliveira, Ana Teresa Freitas · 2026-11-11 05:00

面向具有异步爆发的跨区域流行病预测的联邦学习

arXiv:2611.14272v1 Announce Type: new Abstract: Objective: Epidemic outbreaks are often asynchronous across regions, yet collaborative forecasting is hampered by privacy laws and heterogeneous public health interventions. We developed and evaluated, as a proof of concept, a feder…