Mobile edge computing (MEC) provides an effective solution to help the Internet of Things (IoT) devices with delay-sensitive and computation-intensive tasks by offering computing capabilities in the proximity of mobile device users. Most of the existing studies ignore context information of the application, requests, sensors, resources, and network. However, in practice, context information has a significant impact on offloading decisions. In this paper, we consider context-aware offloading in MEC with multi-user. The contexts are collected using autonomous management as the MAPE loop in all offloading processes. Also, federated learning (FL)-based offloading is presented. Our learning method in mobile devices (MDs) is deep reinforcement learning (DRL). FL helps us to use distributed capabilities of MEC with updated weights between MDs and edge devices (Eds). The simulation results indicate our method is superior to local computing, offload, and FL without considering context-aware algorithms in terms of energy consumption, execution cost, network usage, delay, and fairness.

移动边缘计算（MEC）通过在移动设备用户附近提供计算功能，提供了一种有效的解决方案来帮助物联网（IoT）设备执行延迟敏感和计算密集型任务。现有的大多数研究都忽略了应用程序，请求，传感器，资源和网络的上下文信息。但是，实际上，上下文信息对卸载决策具有重大影响。在本文中，我们考虑了多用户MEC中的上下文感知卸载。在所有卸载过程中，使用自治管理作为MAPE循环来收集上下文。此外，提出了基于联合学习（FL）的卸载。我们在移动设备（MD）中的学习方法是深度强化学习（DRL）。FL帮助我们在MD和边缘设备（Eds）之间使用更新的权重来使用MEC的分布式功能。仿真结果表明，我们的方法在能耗，执行成本，网络使用，延迟和公平性方面都没有考虑上下文感知算法，因此优于本地计算，卸载和FL。