TY - GEN
T1 - Multi-Level ACE-based IoT Knowledge Sharing for Personalized Privacy-Preserving Federated Learning
AU - Wang, Jing
AU - Lin, Xi
AU - Wu, Jun
AU - Mao, Qinghua
AU - Pei, Bei
AU - Li, Jianhua
AU - Guo, Suchang
AU - Zhang, Baitao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The emerging federated learning (FL) enables distributed data mining for Internet of Things (IoT) big data while avoiding data outsourcing privacy risks via local data training and knowledge (i.e., model) sharing. However, only simplified local knowledge sharing will also cause user privacy leaks due to advanced attacks (e.g., model inversion or gradient leakage). Further, how to realize fine-grained and personalized privacy protection for IoT users is still a challenge. In this paper, we first propose a hierarchical cloud-edge orchestrated federated learning architecture for IoT, named HCE-FL, which aims to provide intelligent and distributed data analysis for IoT users. To address the FL privacy issues, we then design a multi-level access control encryption-based IoT knowledge sharing approach for HCE-FL. In our approach, IoT users could be classified into different levels according to their individual privacy requirements. In addition, the proposed multi-level access control encryption algorithm could ensure the confidentiality of the IoT knowledge flow, which runs through local clients, edge sanitizers, and cloud servers in HCE-FL. Moreover, security theoretical analysis shows that our HCE-FL could satisfy'no read' and no write' security rules for the mandatory IoT knowledge access control. Finally, we conduct experiments based on classic MNIST and CIFARIO datasets to evaluate our HCE-FL. The experimental results demonstrate that our solution can achieve personalized privacy-preserving FL without losing IoT data availability and users can obtain better model accuracy and convergence rate through secure IoT knowledge access and sharing.
AB - The emerging federated learning (FL) enables distributed data mining for Internet of Things (IoT) big data while avoiding data outsourcing privacy risks via local data training and knowledge (i.e., model) sharing. However, only simplified local knowledge sharing will also cause user privacy leaks due to advanced attacks (e.g., model inversion or gradient leakage). Further, how to realize fine-grained and personalized privacy protection for IoT users is still a challenge. In this paper, we first propose a hierarchical cloud-edge orchestrated federated learning architecture for IoT, named HCE-FL, which aims to provide intelligent and distributed data analysis for IoT users. To address the FL privacy issues, we then design a multi-level access control encryption-based IoT knowledge sharing approach for HCE-FL. In our approach, IoT users could be classified into different levels according to their individual privacy requirements. In addition, the proposed multi-level access control encryption algorithm could ensure the confidentiality of the IoT knowledge flow, which runs through local clients, edge sanitizers, and cloud servers in HCE-FL. Moreover, security theoretical analysis shows that our HCE-FL could satisfy'no read' and no write' security rules for the mandatory IoT knowledge access control. Finally, we conduct experiments based on classic MNIST and CIFARIO datasets to evaluate our HCE-FL. The experimental results demonstrate that our solution can achieve personalized privacy-preserving FL without losing IoT data availability and users can obtain better model accuracy and convergence rate through secure IoT knowledge access and sharing.
KW - federated learning
KW - Internet of Things
KW - knowledge sharing
KW - privacy protection
UR - http://www.scopus.com/inward/record.url?scp=85197536295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85197536295&partnerID=8YFLogxK
U2 - 10.1109/MSN60784.2023.00124
DO - 10.1109/MSN60784.2023.00124
M3 - Conference contribution
AN - SCOPUS:85197536295
T3 - Proceedings - 2023 19th International Conference on Mobility, Sensing and Networking, MSN 2023
SP - 843
EP - 848
BT - Proceedings - 2023 19th International Conference on Mobility, Sensing and Networking, MSN 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Mobility, Sensing and Networking, MSN 2023
Y2 - 14 December 2023 through 16 December 2023
ER -