A Privacy-Protected Popularity Prediction Scheme for Content Caching Based on Federated Learning
Kailun Wang, Na Deng · IEEE Transactions on Vehicular Technology · 2022
With the explosive growth of mobile data in wireless communication, popular contents need to be brought closer to the edge for rapid data access. Recent studies have proposed methods of content caching based on machine learning but neglected the leakage of private data. To solve this problem, we propose a Private Federated Learning-based Caching (PFLC) scheme. Benefiting from federated learning framework and the pseudo rating matrix (PRM), the scheme can collect the statistical characteristics of a user group to make caching decisions by predicting the popularity of contents without revealing the privacy of users, which means that it can protect the privacy of individual users from being accessed by the server and other users. The performance evaluation demonstrates that the proposed scheme effectively protects individual users’ privacy without sacrificing the cache hit ratio.