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This letter proposes two novel proactive cooperative caching approaches usingdeep learning (DL) to predict users' content demand in a mobile edge cachingnetwork. In the first approach, a (central) content server takesresponsibilities to collect information from all mobile edge nodes (MENs) inthe network and then performs our proposed deep learning (DL) algorithm topredict the content demand for the whole network. However, such a centralizedapproach may disclose the private information because MENs have to share theirlocal users' data with the content server. Thus, in the second approach, wepropose a novel distributed deep learning (DDL) based framework. The DDL allowsMENs in the network to collaborate and exchange information to reduce the errorof content demand prediction without revealing the private information ofmobile users. Through simulation results, we show that our proposed approachescan enhance the accuracy by reducing the root mean squared error (RMSE) up to33.7% and reduce the service delay by 36.1% compared with other machinelearning algorithms.

Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks