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English(EN) Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks

面向隐私的车辆路径规划联邦强化学习

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文提出了一种在车联网中实现通信高效隐私联邦强化学习的新方法。该方法称为相关隐私量化，利用车辆之间的共享随机性来减少模型更新的均方误差，同时保持本地差分隐私并最小化通信成本。该技术旨在通过提高聚合模型更新的准确性并显著减小认证上行链路数据的规模来增强协作路径规划，从而在隐私保护场景中缩短行程时间和提高准确性。 AI

影响 在车联网的联邦强化学习中引入了一种新颖的隐私保护技术，有望提高路径规划效率和数据安全性。

排序理由 arXiv上发表的研究论文，详细介绍了一种新的联邦强化学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

面向隐私的车辆路径规划联邦强化学习

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arXiv上发表的研究论文，详细介绍了一种新的联邦强化学习方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hojat Allah Salehi, Devrim Aras, Md Jueal Mia, M. Hadi Amini, Imtiaz Karim, Mashrur Chowdhury, Bhavani Thuraisingham ·

    Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks

    arXiv:2610.41055v1 Announce Type: new Abstract: Federated reinforcement learning supports cooperative vehicular routing without centralizing trajectory data, but model updates may reveal private information and incur communication overhead over intermittent vehicle-to-infrastruct…

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