  The fog radio access network (F-RAN) is a promising technology in which the
user mobile devices (MDs) can offload computation tasks to the nearby fog
access points (F-APs). Due to the limited resource of F-APs, it is important to
design an efficient task offloading scheme. In this paper, by considering
time-varying network environment, a dynamic computation offloading and resource
allocation problem in F-RANs is formulated to minimize the task execution delay
and energy consumption of MDs. To solve the problem, a federated deep
reinforcement learning (DRL) based algorithm is proposed, where the deep
deterministic policy gradient (DDPG) algorithm performs computation offloading
and resource allocation in each F-AP. Federated learning is exploited to train
the DDPG agents in order to decrease the computing complexity of training
process and protect the user privacy. Simulation results show that the proposed
federated DDPG algorithm can achieve lower task execution delay and energy
consumption of MDs more quickly compared with the other existing strategies.
