{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:34:41Z","timestamp":1768822481221,"version":"3.49.0"},"reference-count":49,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T00:00:00Z","timestamp":1742083200000},"content-version":"vor","delay-in-days":74,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.059472","type":"journal-article","created":{"date-parts":[[2025,1,5]],"date-time":"2025-01-05T21:41:42Z","timestamp":1736113302000},"page":"4649-4667","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Federated Learning and Optimization for Few-Shot Image Classification"],"prefix":"10.32604","volume":"82","author":[{"given":"Yi","family":"Zuo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunhao","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"123149","DOI":"10.1016\/j.eswa.2024.123149","article-title":"Efficient hybrid oversampling and intelligent undersampling for imbalanced big data classification","volume":"246","author":"Vairetti","year":"2024","journal-title":"Expert Syst Appl"},{"key":"ref2","series-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2691","article-title":"Learning from massive noisy labeled data for image classification","author":"Xiao","year":"2015"},{"key":"ref3","unstructured":"Dhillon GS, Chaudhari P, Ravichandran A, Soatto S. A baseline for few-shot image classification. 2019. doi:10.48550\/arXiv.1909.02729."},{"key":"ref4","series-title":"Proceedings of the 7th Machine Learning for Healthcare Conference","first-page":"2","article-title":"Contrastive learning of medical visual representations from paired images and text","author":"Zhang","year":"2022"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"4607","DOI":"10.1109\/TNNLS.2022.3173318","article-title":"Structure-aware prototypical neural process for few-shot graph classification","volume":"35","author":"Lin","year":"2022","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref6","series-title":"ECCV 2020: Computer Vision-ECCV 2020","first-page":"266","article-title":"Rethinking few-shot image classification: a good embedding is all you need?","author":"Tian","year":"2020"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"106854","DOI":"10.1016\/j.cie.2020.106854","article-title":"A review of applications in federated learning","volume":"149","author":"Li","year":"2020","journal-title":"Comput Ind Eng"},{"key":"ref8","series-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3582688","article-title":"A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities","volume":"55","author":"Song","year":"2023","journal-title":"ACM Comput Surv"},{"key":"ref10","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"ref11","series-title":"2019 IEEE Symposium on Security and Privacy (SP)","first-page":"1","article-title":"Comprehensive privacy analysis of deep learning","author":"Nasr","year":"2018"},{"key":"ref12","first-page":"2361","article-title":"FL-EASGD: federated learning privacy security method based on homomorphic encryption","volume":"79","author":"Sun","year":"2024","journal-title":"Comput Mater Contin"},{"key":"ref13","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc Mach Learn Syst"},{"key":"ref14","series-title":"8th International Conference on Learning Representations","first-page":"3678","article-title":"Differentially private meta-learning","author":"Li","year":"2020"},{"key":"ref15","series-title":"2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt)","first-page":"1","article-title":"Federated few-shot learning with adversarial learning","author":"Fan","year":"2021"},{"key":"ref16","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2374","article-title":"Federated few-shot learning","author":"Wang","year":"2023"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"103038","DOI":"10.1016\/j.jprocont.2023.103038","article-title":"Finding trustworthy neighbors: graph aided federated learning for few- shot industrial fault diagnosis with data heterogeneity","volume":"129","author":"Yao","year":"2023","journal-title":"J Process Control"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"28241","DOI":"10.1007\/s10489-023-05006-4","article-title":"Federated few-shot learning for cough classification with edge devices","volume":"53","author":"Hoang","year":"2023","journal-title":"Appl Intell"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/TNNLS.2022.3190359","article-title":"Personalized federated few-shot learning","volume":"35","author":"Zhao","year":"2022","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref20","series-title":"International Conference on Learning Representations","first-page":"5213","article-title":"AugMix: a simple data processing method to improve robustness and uncertainty","author":"Hendrycks","year":"2019"},{"key":"ref21","unstructured":"DeVries T, Taylor GW. Improved regularization of convolutional neural networks with cutout. 2017. doi:10.48550\/arXiv.1708.04552."},{"key":"ref22","series-title":"2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"6023","article-title":"CutMix: regularization strategy to train strong classifiers with localizable features","author":"Yun","year":"2019"},{"key":"ref23","first-page":"3834","article-title":"MaskMix: mask mixing augmentation method for change detection","volume":"40","author":"Xing","year":"2023","journal-title":"Appl Res Comput"},{"key":"ref24","first-page":"5523","article-title":"PatchMix augmentation to identify causal features in few-shot learning","volume":"45","author":"Xu","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref25","series-title":"Proceedings of the International Colloquium on Automata, Languages, and Programming","first-page":"1","article-title":"Differential privacy","author":"Dwork","year":"2006"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/COMST.2019.2944748","article-title":"Differential privacy techniques for cyber physical systems: a survey","volume":"22","author":"Hassan","year":"2019","journal-title":"IEEE Commun Surv Tutor"},{"key":"ref27","series-title":"2019 IEEE 35th International Conference on Data Engineering (ICDE)","first-page":"638","article-title":"Collecting and analyzing multidimensional data with local differential privacy","author":"Wang","year":"2019"},{"key":"ref28","series-title":"2023 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"17580","article-title":"LDP-Feat: image features with local differential privacy","author":"Pittaluga","year":"2023"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"5210","DOI":"10.1109\/JIOT.2023.3306003","article-title":"A LDP-based privacy-preserving longitudinal and multidimensional range query scheme in IOT","volume":"11","author":"Ni","year":"2024","journal-title":"IEEE Internet Things J"},{"key":"ref30","series-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","first-page":"527","article-title":"LDP-FL: practical private aggregation in federated learning with local differential privacy","author":"Sun","year":"2021"},{"key":"ref31","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume":"33","author":"Tian","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref32","series-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"10389","article-title":"Fair contrastive learning for facial attribute classification","author":"Park","year":"2022"},{"key":"ref33","series-title":"48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07)","first-page":"94","article-title":"Mechanism design via differential privacy","author":"McSherry","year":"2007"},{"key":"ref34","first-page":"251","article-title":"TADAM: task dependent adaptive metric for improved few- shot learning","volume":"31","author":"Oreshkin","year":"2018","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref35","unstructured":"Chen X, Fan H, Girshick R, He K. Improved baselines with momentum contrastive learning. 2020. doi:10.48550\/arXiv.2003.04297."},{"key":"ref36","first-page":"8635","article-title":"Learning a few-shot embedding model with contrastive learning","volume":"35","author":"Liu","year":"2021","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref37","first-page":"7527","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int J Comput Vis"},{"key":"ref39","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"Finn","year":"2017"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun ACM"},{"key":"ref41","series-title":"Proceeding of COMPSTAT","first-page":"177","article-title":"Large-scale machine learning with stochastic gradient descent","author":"Bottou","year":"2010"},{"key":"ref42","unstructured":"Goyal P, Doll\u00e1r P, Girshick R, Noordhuis P, Wesolowski L, Kyrola A, et al. Large minibatch SGD: training imageNet in 1 hour. 2010. doi:10.48550\/arXiv.1706.02677."},{"key":"ref43","first-page":"3474","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref44","unstructured":"Hsu TH, Qi H, Brown M. Measuring the effects of non-identical data distribution for federated visual classification. 2019. doi:10.48550\/arXiv.1909.06335."},{"key":"ref45","unstructured":"Yu T, Bagdasaryan E, Shmatikov V. Salvaging federated learning by local adaptation. 2020. doi:10.48550\/arXiv.2002.04758."},{"key":"ref46","series-title":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","first-page":"308","article-title":"Deep learning with differential privacy","volume":"318","author":"Abadi","year":"2016"},{"key":"ref47","unstructured":"McMahan HB, Andrew G, Erlingsson U, Chien S, Mironov I, Papernot N, et al. A general approach to adding differential privacy to iterative training procedures. 2018. doi:10.48550\/arXiv.1812.06210."},{"key":"ref48","doi-asserted-by":"crossref","first-page":"8836","DOI":"10.1109\/JIOT.2020.3037194","article-title":"Local differential privacy-based federated learning for internet of things","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Internet Things J"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"44826","DOI":"10.1109\/ACCESS.2024.3380195","article-title":"DP-FedIOD: a differential privacy and federated learning based framework for aerial insulators orientation detection","volume":"12","author":"Zhang","year":"2024","journal-title":"IEEE Access"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-3\/TSP_CMC_59472\/TSP_CMC_59472.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:30:29Z","timestamp":1763101829000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n3\/59897"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.059472","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-10-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-13","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-06","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}