Video Request Prediction and Cooperative Caching Strategy Based onFederated Learning in Mobile Edge Computing br

With the rise of Internet social platforms and the popularization of mobile smart terminal devices,people's demand for high-quality and real-time data has risen sharply, especially for video services such as shortvideos and live streams. At the same time, too many terminal devices connected to the core network increasethe load of the backhaul link, so that the traditional cloud computing is difficult to meet the low-latencyrequirements of users for video services. By deploying edge nodes with computing and storage capabilities at theedge of the network, Mobile Edge Computing (MEC) can calculate and store closer to the users, which willreduce the data transmission delay and alleviate the network congestion. Therefore, making full use of thecomputing and storage resources at the edge of the network under MEC, a video request prediction method anda cooperative caching strategy based on federated learning are proposed. By federally training the proposedmodel Deep Request Prediction Network (DRPN) with multiple edge nodes, the video requests in the futurecan be predicted and then cache decisions can be made cooperatively. The simulation results show thatcompared with other strategies, the proposed strategy can not only effectively improve the cache hit rate andreduce the user waiting delay, but also reduce the communication cost and cache cost of the whole system to acertain extent