Proactive video caching based on federated learning and implicit feedback in mobile edge computing
Zhen Qian, Guanghui Li, Tao Qi, Chenglong Dai, Jia Wen Hu · 2024
The rapid development of 5G and the widespread use of smart terminal devices bring explosive video traffic growth. Edge caching technology in mobile edge computing systems stores popular content of most interest to users in advance in edge servers closer to mobile users to alleviate the pressure of traffic congestion and excessive access latency caused by centralized storage. However, how to obtain popular videos while protect user data privacy with only implicit user feedback data, such as liking, viewing, favoriting, etc., is the key challenge. To tackle these challenges, we proposed a proactive video caching scheme based on federated learning and implicit feedback (FIPC). First, a mobile edge-cloud system model contained a three-tier network architecture is developed. Then, we introduced the federated process for training the denoised auto-encoder model and video caching in detail. Finally, experimental results in Movielens dataset show that, without user data leakage, the proposed FIPC scheme outperforms the baseline caching algorithm using user feedback data.