Privacy-Preserving Crowd Counting via Quantum-Enhanced Federated Learning

Abstract

Crowd counting plays a crucial role in analyzing group behavior in smart cities. Traditional crowd-counting models rely on large datasets gathered from diverse individuals for training while ignoring the privacy protection for each training client. Meanwhile, the scale variation has long been a difficult problem in crowd counting and has greatly reduced model accuracy. Therefore, it is essential to achieve privacy-aware crowd counting and to solve the problem of scale variation in dense scenes. To this end, we propose a Privacy-preserving Quantum-enhanced Network (PQNet). The PQNet uses federated learning to share parameters rather than data, which ensures the privacy of each client. Subsequently, a multi-scale quantum-driven calibration module is designed to capture multi-scale information via quantum states. It enhances counting accuracy in dense crowd environments where scale varies. Experiments on four crowd counting and two vehicle counting benchmarks demonstrate that PQNet outperforms state-of-the-art methods subjectively and objectively. The code will be available at: https://github.com/sdutzhangchen/PQNet.