With the emergence of a large number of computation-intensive and time-sensitive applications, smart terminal devices with limited resources can only run the model training part of most intelligent applications in the cloud, so a large amount of training data needs to be uploaded to the cloud. This is an important cause of core network communication congestion and poor Quality-of-Experience (QoE) of user. As an important extension and supplement of cloud computing, Mobile Edge Computing (MEC) sinks computing and storage resources from the cloud to the vicinity of User Mobile Devices (UMDs), greatly reducing service latency and alleviating the burden on core networks. However, due to the high cost of edge servers deployment and maintenance, MEC also has the problems of limited network resources and computing resources, and the edge network environment is complex and mutative. Therefore, how to reasonably allocate network resources and computing resources in a changeable MEC environment has become a great aporia. To combat this issue, this paper proposes an intelligent resource allocation model “DRL + FL”. Based on this model, an intelligent resource allocation algorithm DDQN-RA based on the emerging DRL algorithm framework DDQN is designed to adaptively allocate network and computing resources. At the same time, the model integrates the FL framework with the mobile edge system to train DRL agents in a distributed way. This model can well solve the problems of uploading large amounts of training data via wireless channels, Non-IID and unbalance of training data when training DRL agents, restrictions on communication conditions, and data privacy. Experimental results show that the proposed “DRL + FL” model is superior to the traditional resource allocation algorithms SDR and LOBO and the intelligent resource allocation algorithm DRLRA in three aspects: minimizing the average energy consumption of the system, minimizing the average service delay, and balancing resource allocation.

随着大量计算密集型和对时间敏感的应用程序的出现，资源有限的智能终端设备只能在云中运行大多数智能应用程序的模型训练部分，因此需要将大量训练数据上传到云端。这是导致核心网络通信拥塞和用户体验质量（QoE）下降的重要原因。作为云计算的重要扩展和补充，移动边缘计算（MEC）将计算和存储资源从云中转移到用户移动设备（UMD）附近，从而大大减少了服务延迟并减轻了核心网络的负担。但是，由于边缘服务器的部署和维护成本高昂，MEC还存在网络资源和计算资源有限的问题，边缘网络环境复杂多变。因此，如何在多变的MEC环境中合理分配网络资源和计算资源已成为一个很大的难题。为了解决这个问题，本文提出了一种智能资源分配模型“ DRL + FL”。基于该模型，设计了一种基于新兴的DRL算法框架DDQN的智能资源分配算法DDQN-RA来自适应地分配网络和计算资源。同时，该模型将FL框架与移动边缘系统集成在一起，以分布式方式训练DRL代理。该模型可以很好地解决通过DRL代理进行训练时通过无线信道上传大量训练数据，非IID和训练数据不平衡，通信条件限制以及数据隐私等问题。