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In this paper, cooperative edge caching problem is studied in fog radioaccess networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard)property of the problem, a dueling deep Q network (Dueling DQN) based cachingupdate algorithm is proposed to make an optimal caching decision by learningthe dynamic network environment. In order to protect user data privacy andsolve the problem of slow convergence of the single deep reinforcement learning(DRL) model training, we propose a federated reinforcement learning method withquantization (FRLQ) to implement cooperative training of models from multiplefog access points (F-APs) in F-RANs. To address the excessive consumption ofcommunications resources caused by model transmission, we prune and quantizethe shared DRL models to reduce the number of model transfer parameters. Thecommunications interval is increased and the communications rounds are reducedby periodical model global aggregation. We analyze the global convergence andcomputational complexity of our policy. Simulation results verify that ourpolicy has better performance in reducing user request delay and improvingcache hit rate compared to benchmark schemes. The proposed policy is also shownto have faster training speed and higher communications efficiency with minimalloss of model accuracy.

A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks