In this paper, we devise an efficient and flexible management for mobile edge computing (MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the considered IIoT networks, all devices have some computational tasks to be computed with the help of some computational access points (CAPs). Although the performance of the IIoT networks can be optimized by using the resource allocation based on some centralized schemes, such solution is neither efficient nor flexible. To address this issue, we use a deep reinforcement learning (DRL) algorithm based federated learning algorithm to adjust three parameters: the task offloading ratio, bandwidth allocation ratio and transmit power. The optimization can minimize the normalized system cost, while reduce the communication cost in the optimization process. Moreover, simulation results are demonstrated to verify that the proposed federated framework can achieve an efficient and flexible management for the IIoT networks.

在本文中，我们通过联合学习方法设计了一种用于移动边缘计算（MEC）辅助的工业物联网（IIoT）的高效灵活的管理。在考虑的IIoT网络中，所有设备都有一些计算任务，需要借助一些计算访问点（CAP）进行计算。尽管可以通过使用基于一些集中式方案的资源分配来优化IIoT网络的性能，但是这种解决方案既不高效也不灵活。为了解决这个问题，我们使用基于深度强化学习（DRL）的联合学习算法来调整三个参数：任务卸载率，带宽分配率和发射功率。该优化可以最小化归一化的系统成本，同时减少优化过程中的通信成本。而且，