{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:54:09Z","timestamp":1773017649516,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U21A20485"],"award-info":[{"award-number":["U21A20485"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy.<\/jats:p>","DOI":"10.3390\/s23125624","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T02:54:33Z","timestamp":1686884073000},"page":"5624","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Federated Learning-Based Insulator Fault Detection for Data Privacy Preserving"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8260-0584","authenticated-orcid":false,"given":"Zhirong","family":"Luan","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Yujun","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Zhicong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Yu","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, Y., Xin, M., Liao, J., and Xie, Q. (2023). A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5. Sensors, 23.","DOI":"10.20944\/preprints202305.0796.v1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1049\/iet-gtd.2019.1579","article-title":"Analysis of training techniques of ANN for classification of insulators in electrical power systems","volume":"14","author":"Stefenon","year":"2020","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Han, J., Yang, Z., Zhang, Q., Chen, C., Li, H., Lai, S., Hu, G., Xu, C., Xu, H., and Wang, D. (2019). A method of insulator faults detection in aerial images for high-voltage transmission lines inspection. Appl. Sci., 9.","DOI":"10.3390\/app9102009"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhu, H., Bi, L., Xu, W., Song, N., Zhou, Z., Ding, L., and Xiao, M. (2023). Quality Grading of River Crabs Based on Machine Vision and GA-BPNN. Sensors, 23.","DOI":"10.3390\/s23115317"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, G., Shi, G., and Jiao, J. (2023). YOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid Network. Sensors, 23.","DOI":"10.3390\/s23115242"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Zhang, Z. (2023). Research on a Traffic Sign Recognition Method under Small Sample Conditions. Sensors, 23.","DOI":"10.3390\/s23115091"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"11","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35316","DOI":"10.1109\/ACCESS.2018.2846293","article-title":"Insulator fault detection based on spatial morphological features of aerial images","volume":"6","author":"Zhai","year":"2018","journal-title":"IEEE Access"},{"key":"ref_10","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A.y. (2017, January 20\u201322). Communication-efficient learning of deep networks from decentralized data. Proceedings of the Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/s42045-020-00045-8","article-title":"A trusted recommendation scheme for privacy protection based on federated learning","volume":"3","author":"Wang","year":"2020","journal-title":"CCF Trans. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, C., Qin, F., Zhao, W., Li, J., and Liu, T. (2023). Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and Improved ConvNext. Sensors, 23.","DOI":"10.3390\/s23115334"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hong, J.-W., Kim, S.-H., and Han, G.-T. (2023). Detection of Multiple Respiration Patterns Based on 1D SNN from Continuous Human Breathing Signals and the Range Classification Method for Each Respiration Pattern. Sensors, 23.","DOI":"10.3390\/s23115275"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, X., and Zhang, Y. (2016, January 7\u201310). Insulator identification from aerial images using support vector machine with background suppression. Proceedings of the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA.","DOI":"10.1109\/ICUAS.2016.7502544"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.egyr.2022.01.209","article-title":"Lightweight algorithm of insulator identification applicable to electric power engineering","volume":"8","author":"Xing","year":"2022","journal-title":"Energy Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Zhen, Z., Zhang, L., Qi, Y., Kong, Y., and Zhang, K. (2019). Insulator detection method in inspection image based on improved faster R-CNN. Energies, 12.","DOI":"10.3390\/en12071204"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9945","DOI":"10.1109\/ACCESS.2019.2891123","article-title":"Insulator detection in aerial images for transmission line inspection using single shot multibox detector","volume":"7","author":"Miao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"012147","DOI":"10.1088\/1742-6596\/1069\/1\/012147","article-title":"Cracked insulator detection based on R-FCN","volume":"1069","author":"Li","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_20","unstructured":"Geyer, R.C., Klein, T., and Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv."},{"key":"ref_21","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume":"139","author":"Zhu","year":"2021","journal-title":"PMLR"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.knosys.2021.106775","article-title":"A survey on federated learning","volume":"216","author":"Zhang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_23","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y. (2020). Federated learning with matched averaging. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Joshi, R., GG, L.P., Faqeerzada, M.A., Bhattacharya, T., Kim, M.S., Baek, I., and Cho, B.-K. (2023). Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy. Sensors, 23.","DOI":"10.3390\/s23115020"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yu, P., and Liu, Y. (2019, January 26\u201328). Federated object detection: Optimizing object detection model with federated learning. Proceedings of the 3rd International Conference on Vision, Image and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1145\/3387168.3387181"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shokri, R., and Shmatikov, V. (2015, January 12\u201316). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, CO, USA.","DOI":"10.1145\/2810103.2813687"},{"key":"ref_27","first-page":"3973","article-title":"Fedboost: A communication-efficient algorithm for federated learning","volume":"119","author":"Hamer","year":"2020","journal-title":"PMLR"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shanthi, A., and Koppu, S. (2023). Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images. Sensors, 23.","DOI":"10.3390\/s23115316"},{"key":"ref_29","unstructured":"Liu, Z., Luo, P., Qiu, S., Wang, X., and Tang, X. (July, January 26). DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110752","DOI":"10.1016\/j.rser.2021.110752","article-title":"Energy performance, environmental impact and cost of a range of insulation materials","volume":"140","author":"Dickson","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xiao, L., Xu, J., Zhao, D., Shang, E., Zhu, Q., and Dai, B. (2023). Adversarial and Random Transformations for Robust Domain Adaptation and Generalization. Sensors, 23.","DOI":"10.2139\/ssrn.4421672"},{"key":"ref_32","first-page":"32","article-title":"An approach for image annotation automatization for artificial intelligence models learning","volume":"1","author":"Yakovlev","year":"2020","journal-title":"A\u0434anmu\u0432\u043di Cucm. A\u0432mo\u043c. \u0423np."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mufida, M.K., Ait El Cadi, A., Delot, T., Tr\u00e9panier, M., and Zekri, D. (2023). Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models. Sensors, 23.","DOI":"10.3390\/s23115248"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Shukla, S. (2016, January 27\u201328). Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. Proceedings of the 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, India.","DOI":"10.1109\/IACC.2016.25"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lu, T., Wan, L., Qi, S., and Gao, M. (2023). Land Cover Classification of UAV Remote Sensing Based on Transformer\u2013CNN Hybrid Architecture. Sensors, 23.","DOI":"10.3390\/s23115288"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"13364","DOI":"10.1109\/JSEN.2020.3006918","article-title":"A millimetre-wave radar-based fall detection method using line kernel convolutional neural network","volume":"20","author":"Wang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13\u201319). Zero-reference deep curve estimation for low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_38","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e571","DOI":"10.1016\/S2542-5196(21)00210-2","article-title":"Interpersonal violence associated with hot weather","volume":"5","author":"Mahendran","year":"2021","journal-title":"Lancet Planet. Health"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zaman, W., Ahmad, Z., Siddique, M.F., Ullah, N., and Kim, J.-M. (2023). Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN. Sensors, 23.","DOI":"10.3390\/s23115255"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_43","unstructured":"Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z. (2019). On the convergence of FedAvg on non-IID data. arXiv."},{"key":"ref_44","first-page":"7252","article-title":"Bayesian nonparametric federated learning of neural networks","volume":"97","author":"Yurochkin","year":"2019","journal-title":"PMLR"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5624\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:55:59Z","timestamp":1760126159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,15]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125624"],"URL":"https:\/\/doi.org\/10.3390\/s23125624","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,15]]}}}