TY - JOUR
T1 - BC-EdgeFL
T2 - A Defensive Transmission Model Based on Blockchain-Assisted Reinforced Federated Learning in IIoT Environment
AU - Zhang, Peiying
AU - Hong, Yanrong
AU - Kumar, Neeraj
AU - Alazab, Mamoun
AU - Alshehri, Mohammad Dahman
AU - Jiang, Chunxiao
N1 - Funding Information:
The So Cal VOICe also attributes much of its success to its close relationship with the Southern California Vascular Surgery Society (SCVSS). The So Cal VOICe was formed under the auspices of the SCVSS and is supported financially by the society. The medical director of the So Cal VOICe sits on the Board of the SCVSS and is actively involved in any decisions regarding the So Cal VOICe. The spring regional meeting of the So Cal VOICe is held in conjunction with the annual meeting of the SCVSS and all members of the society, whether they are VQI® participants or not, are invited to attend. A segment of the SCVSS program is dedicated annually to an update of the activities of the VQI® and the So Cal VOICe, which encourages SCVSS members who are not VQI® participants to become participants.
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98%. In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.
AB - Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98%. In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.
KW - Blockchain
KW - data transmission method
KW - edge computing (EC)
KW - federated learning (FL)
KW - industrial Internet of Things (IIoT)
UR - http://www.scopus.com/inward/record.url?scp=85124614527&partnerID=8YFLogxK
U2 - 10.1109/TII.2021.3116037
DO - 10.1109/TII.2021.3116037
M3 - Article
AN - SCOPUS:85124614527
SN - 1551-3203
VL - 18
SP - 3551
EP - 3561
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 5
ER -