An active content caching method based on federated learning

Abstract

An active content caching method based on federated learning belongs to the technical field of wireless communication. First, in each round of communication, the user downloads the global model and trains it locally using a stacked autoencoder to obtain the local model and hidden features of users and files. Second, in each round of communication, the user updates the model and sends it to the server, and all local models are aggregated to generate a global model. Third, when the training is over, the user sends the hidden features of the user and the file to the server. The server first calculates the user similarity and file similarity, then randomly selects a user, and uses the decoder of the stacked autoencoder recover their pseudo-scoring matrix. Finally, collaborative filtering is used to calculate the scores of this group of users on all files, and the files with the highest average score are selected for caching. On the premise of ensuring the cache hit rate, the present invention effectively avoids the data sharing problem among neighbor users and makes the private data of users more secure.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and relates to an active content caching method based on federated learning. Background technique [0002] At present, mobile data is facing explosive growth, the total amount of data is large, and the search and transmission time for data is long. Therefore, it is necessary to filter data and make useful data close to the user side to achieve fast data access. Wireless network content caching technology emerges at the historic moment, it is very helpful to reduce backhaul traffic load and reduce service delay of mobile users under the background of the surge of mobile data traffic. Since the capacity of content caching devices is limited, it is important to predict which files are worth caching. However, most traditional content caching algorithms are passive, and only respond to the access requests that have occurred, without considering the popularity of future content, such as...

AI Technical Summary

Problems solved by technology

Although these two types of centralized learning methods improve the cache efficiency, there are two problems: first, in wireless communication networks, data is generated by billions of devices

For such large-scale data, if you want to maintain the efficiency of the algorithm, you need to rely on a powerful central machine learning processor, and at the same time face huge communication transmission overhead, which makes large-scale learning difficult to achieve in reality.

Second, because the user's historical demand will involve the user's privacy in most cases, the user is unwilling to share the data containing their own privacy. Therefore, the user's distrust of the server makes it very difficult to collect historical demand data

At this time, data sharing depends on the intermediate medium. However, even if encrypted transmission is adopted, the possibility of external data leakage will still increase because the data is likely to be attacked or intercepted during the transmission process.

Images