{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T20:20:06Z","timestamp":1769113206738,"version":"3.49.0"},"reference-count":36,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.064489","type":"journal-article","created":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T08:46:29Z","timestamp":1746261989000},"page":"1839-1861","source":"Crossref","is-referenced-by-count":1,"title":["An Optimized Unsupervised Defect Detection Approach via Federated Learning and Adaptive Embeddings Knowledge Distillation"],"prefix":"10.32604","volume":"84","author":[{"given":"Jinhai","family":"Wang","sequence":"first","affiliation":[]},{"given":"Junwei","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Hongyan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Huiling","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Mingyou","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jiang","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Lufeng","family":"Luo","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"116702","DOI":"10.1016\/j.measurement.2025.116702","article-title":"Accelerating surface defect detection using normal data with an attention-guided feature distillation reconstruction network","volume":"246","author":"Wang","year":"2025","journal-title":"Measurement"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"116657","DOI":"10.1016\/j.measurement.2025.116657","article-title":"Aircraft skin defect detection based on Fourier GAN data augmentation under limited samples","volume":"245","author":"Li","year":"2025","journal-title":"Measurement"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.jmrt.2025.01.045","article-title":"Advances in subsurface defect detection techniques for fused silica optical components: a literature review","volume":"35","author":"Cao","year":"2025","journal-title":"J Mater Res Technol"},{"key":"ref4","first-page":"1","article-title":"Joining spatial deformable convolution and a dense feature pyramid for surface defect detection","volume":"73","author":"Huang","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref5","series-title":"Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"MVTec AD\u2014a comprehensive real-world dataset for unsupervised anomaly detection","author":"Bergmann","year":"2019 Jun 15\u201320"},{"key":"ref6","series-title":"Proceedings of the International Conference on Pattern Recognition 2021","article-title":"Padim: a patch distribution modeling framework for anomaly detection and localization","author":"Defard","year":"2021 Jan 10\u201311"},{"key":"ref7","unstructured":"Hsu TMH, Qi H, Brown M. Measuring the effects of non-identical data distribution for federated visual classification. arXiv:1909.06335. 2019."},{"key":"ref8","series-title":"Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Towards total recall in industrial anomaly detection","author":"Roth","year":"2022 Jun 18\u201324"},{"key":"ref9","series-title":"Proceedings of the 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","article-title":"Fully convolutional cross-scale-flows for image-based defect detection","author":"Rudolph","year":"2022 Jan 3\u20138"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"112452","DOI":"10.1016\/j.measurement.2023.112452","article-title":"Active federated transfer algorithm based on broad learning for fault diagnosis","volume":"208","author":"Liu","year":"2023","journal-title":"Measurement"},{"key":"ref11","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"McMahan","year":"2017","journal-title":"Proc Mach Learn Res"},{"key":"ref12","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume":"33","author":"Lin","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Islam F, Raihan AS, Ahmed I. Applications of federated learning in manufacturing: identifying the challenges and exploring the future directions with Industry 4.0 and 5.0 visions. arXiv:2302.13514. 2023.","DOI":"10.46254\/NA8.20230219"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.jmsy.2022.06.010","article-title":"Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing","volume":"64","author":"Mehta","year":"2022","journal-title":"J Manuf Syst"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1631\/FITEE.2200268","article-title":"Federated unsupervised representation learning","volume":"24","author":"Zhang","year":"2023","journal-title":"Front Inf Technol Electron Eng"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"6984","DOI":"10.1109\/TASE.2024.3457829","article-title":"SDD-DETR: surface defect detection for no-service aero-engine blades with detection transformer","volume":"22","author":"Sun","year":"2024","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TII.2024.3470895","article-title":"SRPCNet: self-reinforcing perception coordination network for seamless steel pipes internal surface defect detection","volume":"21","author":"Chen","year":"2024","journal-title":"IEEE Trans Ind Inform"},{"key":"ref18","first-page":"1","article-title":"Fine-grained tiny defect detection in spiral welds: a joint framework combining semantic discrimination and contrast transformation","volume":"74","author":"Cui","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"9725","DOI":"10.1109\/TII.2024.3383513","article-title":"MFANet: multifeature aggregation network for cross-granularity few-shot seamless steel tubes surface defect segmentation","volume":"20","author":"Song","year":"2024","journal-title":"IEEE Trans Ind Inform"},{"key":"ref20","series-title":"Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Latent space autoregression for novelty detection","author":"Abati","year":"2019 Jun 15\u201320"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","article-title":"f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks","volume":"54","author":"Schlegl","year":"2019","journal-title":"Med Image Anal"},{"key":"ref22","series-title":"Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Simplenet: a simple network for image anomaly detection and localization","author":"Liu","year":"2023 Jun 17\u201324"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1111\/mice.12970","article-title":"Transformer-optimized generation, detection, and tracking network for images with drainage pipeline defects","volume":"38","author":"Ma","year":"2023","journal-title":"Comput Aided Civ Infrastruct Eng"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"15170","DOI":"10.1038\/s41598-024-66234-3","article-title":"Defect detection of photovoltaic modules based on improved VarifocalNet","volume":"14","author":"Jia","year":"2024","journal-title":"Sci Rep"},{"key":"ref25","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc Mach Learn Syst"},{"key":"ref26","unstructured":"Li X, Jiang M, Zhang X, Kamp M, Dou Q. Fedbn: federated learning on non-iid features via local batch normalization. arXiv:2102.07623. 2021."},{"key":"ref27","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume":"33","author":"Wang","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref28","first-page":"5132","article-title":"Scaffold: stochastic controlled averaging for federated learning","volume":"119","author":"Karimireddy","year":"2020","journal-title":"Proc Mach Learn Res"},{"key":"ref29","unstructured":"Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. arXiv:1503.02531. 2015."},{"key":"ref30","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","volume":"139","author":"Touvron","year":"2021","journal-title":"Proc Mach Learn Res"},{"key":"ref31","series-title":"Proceedings of the European Conference on Computer Vision 2022","article-title":"A fast knowledge distillation framework for visual recognition","author":"Shen","year":"2022 Oct 23\u201327"},{"key":"ref32","unstructured":"Hao Z, Guo J, Han K, Hu H, Xu C, Wang Y. Vanillakd: revisit the power of vanilla knowledge distillation from small scale to large scale. arXiv:2305.15781. 2023."},{"key":"ref33","unstructured":"Li D, Wang J. Fedmd: heterogenous federated learning via model distillation. arXiv:1910.03581. 2019."},{"key":"ref34","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TC.2023.3315066","article-title":"FedGKD: toward heterogeneous federated learning via global knowledge distillation","volume":"73","author":"Yao","year":"2023","journal-title":"IEEE Trans Comput"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s00371-018-1588-5","article-title":"Surface defect saliency of magnetic tile","volume":"36","author":"Huang","year":"2020","journal-title":"Vis Comput"},{"key":"ref36","series-title":"Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Model-contrastive federated learning","author":"Li","year":"2021 Jun 20\u201325"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-1\/TSP_CMC_64489\/TSP_CMC_64489.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:36:00Z","timestamp":1763343360000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n1\/61766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.064489","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}