Citation: | MA Xue and WEN Chenglin, “An Asynchronous Quasi-Cloud/Edge/Client Collaborative Federated Learning Mechanism for Fault Diagnosis,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 969-977, 2021, doi: 10.1049/cje.2021.07.008 |
Andrea, Calderaro, "Book review:Big data:A revolution that will transform how we live, work, and think", Media, Culture & Society, Vol.37, No.7, pp.1113-1115, 2015.
|
V. Grover, R. H. L. Chiang, T. P. Liang, et al., "Creating strategic business value from big data analytics:A research framework", Journal of Management Information Systems, Vol.35, No.2, pp.388-423, 2018.
|
J. Zhang and D. Tao, "Empowering things with intelligence:A survey of the progress, challenges, and opportunities in artificial intelligence of things", IEEE Internet of Things Journal, DOI:10.1109/JIOT.2020.3039359, 2020.
|
H. Pan, J. Zheng, Y. Yang and B. Hong, "Research on intelligent fault diagnosis method based on CELCD and MFVPMCD", Acta Electronica Sinica, Vol.45, No.03, pp.546-551, 2017. (in Chinese)
|
J. Zheng, H. Pan, X. Qi, et al., "Time-frequency analysis method based on improved empirical wavelet transform and its application in rolling bearing fault diagnosis", Acta Electronica Sinica, Vol.46, No.02, pp.358-364, 2018. (in Chinese)
|
K. A. Houser and W. G. Voss, "GDPR:The end of google and facebook or a new paradigm in data privacy", Richmond Journal of Law and Technology, Vol.25, No.1, pp.1-109, 2018.
|
H. Zhu and Y. Jin, "Multi-objective evolutionary federated learning", IEEE Transactions on Neural Networks, Vol.28, No.12, pp.1-13, 2019.
|
E. C. Martins and F. G. Jota, "Design of networked control systems with explicit compensation for time-delay variations", IEEE Transactions on Systems Man & Cybernetics, Part, C, Vol.40, No.3, pp.308-318, 2010.
|
Q. Yang, Y. Liu, T. Chen and Y. Tong, "Federated machine learning:Concept and applications", ACM Transactions on Intelligent Systems and Technology, Vol. 10, No. 2, pp.1-19, 2019.
|
N. I. Mowla, N. H. Tran, I. Doh and K. Chae, "Federated learning-based cognitive detection of jamming attack in flying ad-hoc network", IEEE Access, Vol.8, No.1, pp.4338-4350, 2020.
|
R. C. Geyer, T. Klein and M. Nabi, "Differentially private federated learning a client level perspective", 31st Conference on Neural Information Processing Systems, Vol.15, No.99, pp.911-926, 2017.
|
S. Caldas, K. Jakub, H. B. Mcmahan, et al., "Expanding the reach of federated learning by reducing client resource requirements", IEEE Journal on Selected Areas in Communications, Vol.37, No.6, pp.1205-1221, 2018.
|
M. Xia, T. Li, L. Xu, et al., "Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks", IEEE-ASME Transactions on Mechatronics, Vol.23, No.1, pp.101-110, 2017.
|
A. Bharadwaj and S. Ghosh, "Data reconstruction at surface in immersed-boundary methods", Computers & Fluidsvol, Vol.196, No.1, pp.1-18, 2019.
|
X. Hu, W. Pedrycz, G. Wu, et al., "Data reconstruction with information granules:An augmented method of fuzzy clustering", Applied Soft Computing, Vol.55, No.1, pp.523-532, 2017.
|
S. Jeong, M. Ferguson, R. Hou, et al., "Sensor data reconstruction using bidirectional recurrent neural network with application to bridge monitoring", Advanced Engineering Informatics, Vol.42, No.1, pp.1-14, 2019.
|
J. Zhang, J. Tian, T. Wen, et al., "Deep fault diagnosis for rotating machinery with scarce labeled samples", Chinese Journal of Electronics, Vol.29, No.04, pp.666-672, 2020.
|
T. Wen, C. Constantinou, L. Chen, et al., "A practical access point deployment optimization strategy in communicationbased train control systems", IEEE Transactions on Intelligent Transportation Systems, Vol.20, No.8, pp.3156-3167, 2019.
|
X. Guo, L. Chen and C. Shen, "Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis", Measurement, Vol.93, No.01, pp.490-502, 2016.
|
Y. Lu, X. Huang, Y. Dai, et al., "Blockchain and federated learning for privacy-preserved data sharing in industrial iot", IEEE Transactions on Industrial Informatics, Vol.16, No.6, pp.4177-4186, 2020.
|
G. Li, Z. Zhou, C. Hu, et al., "A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base", Safety Science, Vol.93, No.01, pp.108-120, 2017.
|
W. S. Mcculloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity", IEEE Transactions on Industrial Informatics, Vol.5, No.4, pp.115-133, 1943.
|
S. Gannot, D. Burshtein and E. Weinstein, "Iterative and sequential kalman filter-based speech enhancement algorithms", IEEE Transactions on Speech and Audio Processing, Vol.6, No.4, pp.373-385, 1998.
|
V. D. M. Laurens and G. Hinton, "Visualizing data using TSNE", Journal of Machine Learning Research, Vol.9, No.1, pp.2579-2605, 2008.
|