Vertical Federated Learning Based Privacy-Preserving Cooperative Sensing in Cognitive Radio Networks
Yirun Zhang, Qirui Wu, Mohammad Shikh‐Bahaei · 2020
Machine learning-based cooperative sensing scheme, despite its effectiveness in significantly improving the sensing performance, suffers from privacy threats because the sensing reports shared by secondary users (SUs) are highly correlated to their locations, which can be maliciously exploited to infer private information. In this paper, we propose a novel vertical federated learning-based cooperative sensing (VFL-CS) scheme where sensing results are kept locally at each smart SU (SSU) and the model is trained in a decentralised collaborative learning setting. A multi-user deep learning-based FL architecture is constructed with detailed training and evaluation processes explained and security analysed. Simulation results show that our proposed VFL-CS scheme outperforms conventional soft-fusion based cooperative sensing (SF-CS) scheme in terms of much higher area under curve (AUC) score with high data privacy-preserving capability.