  With the development of the Internet of Things (IoT) and the birth of various
new IoT devices, the capacity of massive IoT devices is facing challenges.
Fortunately, edge computing can optimize problems such as delay and
connectivity by offloading part of the computational tasks to edge nodes close
to the data source. Using this feature, IoT devices can save more resources
while still maintaining the quality of service. However, since computation
offloading decisions concern joint and complex resource management, we use
multiple Deep Reinforcement Learning (DRL) agents deployed on IoT devices to
guide their own decisions. Besides, Federated Learning (FL) is utilized to
train DRL agents in a distributed fashion, aiming to make the DRL-based
decision making practical and further decrease the transmission cost between
IoT devices and Edge Nodes. In this article, we first study the problem of
computation offloading optimization and prove the problem is an NP-hard
problem. Then, based on DRL and FL, we propose an offloading algorithm that is
different from the traditional method. Finally, we studied the effects of
various parameters on the performance of the algorithm and verified the
effectiveness of both the DRL and FL in the IoT system.
