Cloud-edge-terminal collaborative network (CETCN) has become a key enabler of the next generation wireless network. However, due to the privacy concern, terminals and the edge server may not will to leakage individual data to the cloud server. At the same time, limited computing capability of the edge server and terminals leads to long latency. Therefore, in this paper, we utilize a federated deep reinforce learning (DRL) algorithm named federated learning-based Double Deep Q Network (FL-DDQN) algorithm to solve the task offloading problem in CETCN. To be specific, firstly, we model the task offloading issue as the minimization problem of weighted energy consumption and latency of the CETCN with the constraints of subcarriers and maximum task processing latency. Secondly, we propose a DRL algorithm to obtain the suboptimal solution of the optimization problem. Thirdly, aiming to improve data security and reduce the working pressure of the terminals and the edge server, the FL-DDQN algorithm is further utilized, where the DDQN model is trained cooperatively in the terminals and the edge server. Finally the simulation result demonstrate that our proposed method superior to benchmark solutions in terms of total energy consumption and latency.

In this paper, we design a caching replacement algorithm for edge networks to enhance the cache hit ratio of the edge network and reduce the traffic usage in the backbone network. Firstly, we design a dynamic-game model to represent the caching behavior between edge servers where each server takes the cache content of other servers into account to make their own caching decisions. Secondly, we analyze the utility function of edge servers from the perspective of caching contents. Finally, we derive the Nash equilibrium solution of the dynamic-game model and design a caching replacement algorithm to obtain the Nash equilibrium solution. Numerical results prove that the proposed edge caching replacement algorithm based on dynamic-game model improves the edge hit ratio up to 80% and meanwhile reduces 40% backbone traffic compared to the baseline solution.