Beijing University of Technology

Ziyi Teng

Juan Fang

Huijing Yang

Xuan Wang

Shuaibing Lu

Min Cai

The invention relates to an optimization method for mobile edge cache based on federated learning. 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 includes 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.

Method for federated-learning-based mobile edge cache optimization

The invention discloses a prefetch-adaptive intelligent cache replacement policy for high performance, in the presence of hardware prefetching, a prefetch request and a demand request are distinguished, a prefetch predictor based on an ISVM (Integer Support Vector Machine) is used for carrying out re-reference interval prediction on a cache line of prefetching access loading, and a demand predictor based on an ISVM is utilized to carry out re-reference interval prediction on a cache line of demand access loading. A PC of a current access load instruction and PCs of past load instructions in an access historical record are input, different ISVM predictors are designed for prefetch and demand requests, reuse prediction is performed on a loaded cache line by taking a request type as granularity, the accuracy of cache line reuse prediction in the presence of prefetching is improved, and performance benefits from hardware prefetching and cache replacement is better fused.

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BEIJING, China

Ziyi Teng