Epidemic Decision-making System Based Federated Reinforcement Learning

📄 arXiv: 2311.01749v1 📥 PDF

作者: Yangxi Zhou, Junping Du, Zhe Xue, Zhenhui Pan, Weikang Chen

分类: cs.LG, cs.IR

发布日期: 2023-11-03

💡 一句话要点

提出基于联邦强化学习的疫情决策系统以应对公共卫生挑战

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 疫情决策 联邦学习 强化学习 数据隐私 协同训练 公共卫生 模型优化

📋 核心要点

  1. 现有疫情决策方法面临样本有限和数据隐私保护的挑战，影响决策的准确性和有效性。
  2. 本文提出了一种基于联邦强化学习的模型，能够在保护数据隐私的前提下进行协同训练，优化疫情决策。
  3. 实验结果显示，增强的联邦学习在性能和收敛速度上优于传统方法，A2C模型在疫情决策中表现最佳。

📝 摘要（中文）

疫情决策能够有效帮助政府综合考虑公共安全与经济发展，以应对公共卫生和安全紧急情况。已有研究表明，强化学习能够有效支持政府进行疫情决策，从而实现健康安全与经济发展的平衡。然而，疫情数据通常具有样本有限和隐私保护的特点。本文提出了一种结合各省疫情数据进行协同训练的模型，以保护数据隐私的同时优化疫情决策。实验结果表明，增强的联邦学习在性能和训练收敛速度上均优于传统学习方法，A2C模型在疫情决策场景中表现最佳。

🔬 方法详解

问题定义：本文旨在解决疫情决策中数据样本有限和隐私保护的挑战，现有方法难以有效利用分散的疫情数据进行决策优化。

核心思路：通过引入联邦强化学习，结合各省的疫情数据进行协同训练，确保数据隐私的同时提升决策模型的性能。

技术框架：整体架构包括数据收集、模型训练、隐私保护和决策输出四个主要模块，采用联邦学习机制进行模型更新。

关键创新：提出的增强联邦学习方法在隐私保护的基础上，实现了多省数据的有效整合与协同训练，显著提升了决策模型的性能。

关键设计：在模型训练中，采用A2C作为主要强化学习算法，设置了适当的损失函数和网络结构，以加速训练收敛并优化决策效果。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，增强的联邦学习方法在性能上优于传统强化学习方法，尤其是A2C模型在疫情决策场景中表现最佳，训练收敛速度显著加快，提升幅度明显。

🎯 应用场景

该研究的潜在应用领域包括公共卫生管理、疫情应对策略制定及政府决策支持系统。通过优化疫情决策，能够有效提升公共安全和经济发展的平衡，为未来类似公共卫生事件的应对提供参考。

📄 摘要（原文）

Epidemic decision-making can effectively help the government to comprehensively consider public security and economic development to respond to public health and safety emergencies. Epidemic decision-making can effectively help the government to comprehensively consider public security and economic development to respond to public health and safety emergencies. Some studies have shown that intensive learning can effectively help the government to make epidemic decision, thus achieving the balance between health security and economic development. Some studies have shown that intensive learning can effectively help the government to make epidemic decision, thus achieving the balance between health security and economic development. However, epidemic data often has the characteristics of limited samples and high privacy. However, epidemic data often has the characteristics of limited samples and high privacy. This model can combine the epidemic situation data of various provinces for cooperative training to use as an enhanced learning model for epidemic situation decision, while protecting the privacy of data. The experiment shows that the enhanced federated learning can obtain more optimized performance and return than the enhanced learning, and the enhanced federated learning can also accelerate the training convergence speed of the training model. accelerate the training convergence speed of the client. At the same time, through the experimental comparison, A2C is the most suitable reinforcement learning model for the epidemic situation decision-making. learning model for the epidemic situation decision-making scenario, followed by the PPO model, and the performance of DDPG is unsatisfactory.