关键词: 边缘缓存, 联邦学习, 内容推荐, 雾计算网络

Abstract: With the rapid development of Internet of Things technology, explosive end-user business demands have brought great challenges to 5G networks. In order to reduce the delay of content acquisition, while protecting user privacy and improving user experience, this paper proposes a content caching scheme based on federated learning in fog computing networks to reduce the content acquisition delay. Firstly, a Device-To-Device (D2D) collaborative fog computing network model is proposed. Users can obtain content from the user, fog node and cloud through D2D and wireless link; Secondly, the user builds a deep neural network model locally, trains the local model using the historical request data, and FN aggregates the local models to predict the global content popularity; At the same time, it provides users with personalized content recommendation list to improve cache hit rate. Finally, based on the real data set, the simulation results show that this scheme can effectively reduce the content acquisition delay and improve the cache hit rate.

Key words: Edge Caching, Federated Learning, Content Recommendation, Fog computing network