Privacy-preserving federated k-means for proactive caching in next generation cellular networks

Abstract

Proactive caching is a novel smart communication resource management method that can offer intelligent and economic networking services in the next generation cellular networks. In proactive caching, a common operation is using k-means to estimate content popularity. However, during the process, the base stations have to collect user’s location and content preference information to train a k-means model, which causes user privacy leakage. And current privacy-preserving k-means schemes usually suffer dramatic user quality of experience reduction, and cannot deal with the user dropout condition. Therefore, we propose a privacy-preserving federated k-means scheme (named PFK-means) for proactive caching in the next generation cellular networks. PFK-means is based on two privacy-preserving techniques, federated learning and secret sharing. In PFK-means, a suite of secret sharing protocols are designed to lightweight and efficient federated learning of k-means. These protocols allow privacy-preserving k-means training for proactive caching when there are dropout users. We seriously analyze the security of PFK-means and conduct comprehensive experiments to prove its security, effectiveness and efficiency. Through comparison, we can conclude that PFK-means outperforms other existing related schemes.