TY - CHAP
T1 - Vertical Federated Learning Based Privacy-Preserving Cooperative Sensing in Cognitive Radio Networks
AU - Zhang, Yirun
AU - Wu, Qirui
AU - Shikh-Bahaei, Mohammad
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - additively homomorphic encryption
KW - Cognitive radio
KW - deep learning
KW - federated learning
KW - secure cooperative sensing
UR - http://www.scopus.com/inward/record.url?scp=85102921484&partnerID=8YFLogxK
U2 - 10.1109/GCWkshps50303.2020.9367398
DO - 10.1109/GCWkshps50303.2020.9367398
M3 - Conference paper
AN - SCOPUS:85102921484
T3 - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
BT - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Globecom Workshops, GC Wkshps 2020
Y2 - 7 December 2020 through 11 December 2020
ER -