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Epidemic decision-making can effectively help the government tocomprehensively consider public security and economic development to respond topublic health and safety emergencies. Epidemic decision-making can effectivelyhelp the government to comprehensively consider public security and economicdevelopment to respond to public health and safety emergencies. Some studieshave shown that intensive learning can effectively help the government to makeepidemic decision, thus achieving the balance between health security andeconomic development. Some studies have shown that intensive learning caneffectively help the government to make epidemic decision, thus achieving thebalance between health security and economic development. However, epidemicdata often has the characteristics of limited samples and high privacy.However, epidemic data often has the characteristics of limited samples andhigh privacy. This model can combine the epidemic situation data of variousprovinces for cooperative training to use as an enhanced learning model forepidemic situation decision, while protecting the privacy of data. Theexperiment shows that the enhanced federated learning can obtain more optimizedperformance and return than the enhanced learning, and the enhanced federatedlearning can also accelerate the training convergence speed of the trainingmodel. accelerate the training convergence speed of the client. At the sametime, through the experimental comparison, A2C is the most suitablereinforcement learning model for the epidemic situation decision-making.learning model for the epidemic situation decision-making scenario, followed bythe PPO model, and the performance of DDPG is unsatisfactory.

Epidemic Decision-making System Based Federated Reinforcement Learning