TY - GEN
T1 - Achieving Secure Federated Learning Assisted by Covert Communication
AU - Jiang, Anguo
AU - Zhou, Huan
AU - Chen, Rui
AU - Leung, Victor
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Federated learning (FL) is widely utilized in machine learning by exploiting its capabilities of protecting data privacy. However, due to the openness and broadcast characteristics of wireless channels, the parameters of FL is facing serious security risks during the upload process. Considering the protection of covert communication on communication behavior, a secure FL framework with the aid of covert communication is proposed, which realizes the covert transmission of parameters. Specifically, edge server (ES) sends artificial noise to confuse a malicious eavesdropper (Eve) when mobile devices (MDs) transmit information. Thus, we investigate the joint optimization problem of MDs power and jamming power to minimize the total latency in FL for a given covert constraint. Then, a particle swarm optimisation (PSO) algorithm is proposed in order to address the optimization problem. The results indicate that the proposed framework can successfully achieve covert transmission of model update parameters, thereby enhancing the overall security of the system.
AB - Federated learning (FL) is widely utilized in machine learning by exploiting its capabilities of protecting data privacy. However, due to the openness and broadcast characteristics of wireless channels, the parameters of FL is facing serious security risks during the upload process. Considering the protection of covert communication on communication behavior, a secure FL framework with the aid of covert communication is proposed, which realizes the covert transmission of parameters. Specifically, edge server (ES) sends artificial noise to confuse a malicious eavesdropper (Eve) when mobile devices (MDs) transmit information. Thus, we investigate the joint optimization problem of MDs power and jamming power to minimize the total latency in FL for a given covert constraint. Then, a particle swarm optimisation (PSO) algorithm is proposed in order to address the optimization problem. The results indicate that the proposed framework can successfully achieve covert transmission of model update parameters, thereby enhancing the overall security of the system.
KW - Data privacy
KW - covert communication
KW - differential privacy
KW - federated learning
UR - http://www.scopus.com/inward/record.url?scp=85188250918&partnerID=8YFLogxK
U2 - 10.1109/AIoTSys58602.2023.00035
DO - 10.1109/AIoTSys58602.2023.00035
M3 - 会议稿件
AN - SCOPUS:85188250918
T3 - Proceedings - 2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023
SP - 91
EP - 96
BT - Proceedings - 2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023
Y2 - 19 October 2023 through 22 October 2023
ER -