公开(公告)号：US20240427700A1

公开(公告)日：2024-12-26

申请号：US18702683

申请日：2022-05-13

Applicant: Beijing University of Technology

Inventor： Juan Fang ,  Ziyi Teng ,  Huijing Yang ,  Shuaibing Lu

IPC: G06F12/0802

Abstract: The invention relates to an optimization method for mobile edge cache based on federated learning, and belongs to the field of Internet of things and artificial intelligence. According to the method, the situation that the user mobility and the content popularity change continuously in the range of a single base station is considered, and the cache hit rate is increased by predicting the content popularity and placing the request content in an edge cache in advance. The method specifically comprises the steps of obtaining a user moment trajectory table to simulate a moving path of a user by using an RWP random path point model, selecting the user participating in FL local training in a clustering, and threshold value combination mode in consideration of local training consumption, performing global model aggregation by using an attention mechanism to control model weight, and performing global prediction according to an obtained global prediction model. The predicted request content is cached to the server in advance to improve the cache hit rate. According to the method, a federated learning method is utilized; user selection and weight aggregation are optimized; and the effective federated learning method is implemented, so that the local training consumption is reduced, and the cache hit rate is increased.