ABSTRACT
In this paper, an invariance constraint-based federated learning method is proposed to implement the distributed cloud detection for 5G charging devices. In order to solve the problem that the charging devices have large numbers and scattered distribution, a distributed scenario modeling of the charging devices is proposed. Each charging device is regarded as a child node, and a central node is constructed on cloud end. In this modeling scenario, the 5G network is used to transmit only the model and gradient parameters between the central node and each child node, which avoids high cost of data transmission and high burden of centralized data storage. In addition, the invariance-based loss function is designed to solve the problem that the data distribution of each child node has significant statistical heterogeneity in the train stage. The proposed method makes the model generalize better, and improves the accuracy of the safety detection of charging devices.
- Donglan Liu, Hao Zhang, Sihan Lu, Fangzhe Zhang and Lili Sun. 2022. Design and Application of Power Internet of Things Device Security Detection System. Shandong Electric Power 49, 9 (September 2022), 72-76.Google Scholar
- Dongqi Lu, Qian Zhang, Yizhou Xu, Jieli Bao and Fen Luo. 2021. Terminal Equipment Adaptive Access for Power Internet of Things. Journal of Shanghai Jiao Tong University 55, s2 (December 2021), 29-35. DOI:10.16183/j.cnki.jsjtu.2021.S2.011Google ScholarCross Ref
- Sumin Yu, Yang Du, Yiwei Shi, Hao Su, Donghan Fen and Henglie Li. 2021. Optimal scheduling of low-carbon building considering V2B smart charging pile groups. Electric Power Automation Equipment 41, 9 (September 2021), 95-101. DOI:10.16081/j.epae.202109021Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR ’16). IEEE, Las Vegas, 770-778.Google Scholar
- Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations (ICLR ’15). San Diego.Google Scholar
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet and Scott Reed. 2016. Going Deeper with Convolutions. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR ’15). IEEE, Boston, 1-9.Google Scholar
- Durmus A. E. A., Yue Z., Ramon M., Matthew M., Paul W. and Venkatesh S. 2021. Federated Learning Based on Dynamic Regularization. In International Conference on Learning Representations (ICLR ’21). Vienna.Google Scholar
- Sai P. K., Satyen K., Mehryar M., Sashank J. R., Sebastian U. S. and Ananda T. S.. 2020. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning (PMLR ’20). Vienna, 5132-5143.Google Scholar
- Tian L., Anit K. S., Manzil Z., Maziar S., Ameet T. and Virginia S.. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine and Systems 2 , 429-450.Google Scholar
- H. B. M.,Eider M., Daniel R., Seth H. and Blaise A. A.. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (PMLR ’17). Seattle, 1273-1282.Google Scholar
Index Terms
- Distributed Cloud Detection Method for 5G Charging Devices Based on Invariance Constraint
Recommendations
Panoramic image style transfer technology based on multi-attention fusion
CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software EngineeringMoving-Target Defense for Detecting Coordinated Cyber-Physical Attacks in Power Grids via a Modified Sensor Measurements Expression
CSW '22: Proceedings of the 2022 International Conference on Cyber SecurityThis paper proposes a modified sensor measurement expression for moving target defense (MTD) method to detect coordinated cyber-physical attacks(CCPAs). As a new type of attack, CCPAs are considerably harmful. Through elaborately designing a coordinated ...
EV charging: separation of green and brown energy using IoT
UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: AdjunctThe energy drivers use to charge their Electric Vehicles (EVs) comes from various sources. Of those, some are renewable green energy sources such as solar photovoltaic systems (SolarPV) and home storage battery whilst some are called brown sources such ...
Comments