{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:12:26Z","timestamp":1751515946814,"version":"3.41.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62206238"],"award-info":[{"award-number":["62206238"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20220562","BK20220562","BK20220562"],"award-info":[{"award-number":["BK20220562","BK20220562","BK20220562"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Research Project of Universities in Jiangsu Province","award":["22KJB520010"],"award-info":[{"award-number":["22KJB520010"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M732985"],"award-info":[{"award-number":["2023M732985"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10044-025-01453-6","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T09:44:09Z","timestamp":1746524649000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedPG: a privacy-friendly and universal method for solving non-IID data in federated learning"],"prefix":"10.1007","volume":"28","author":[{"given":"Baolu","family":"Xue","sequence":"first","affiliation":[]},{"given":"Jiale","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Weizhi","family":"Meng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"1453_CR1","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115:211\u2013252","journal-title":"Int J Comput Vision"},{"key":"1453_CR2","doi-asserted-by":"crossref","unstructured":"Xiong H, Yan H, Obaidat MS, Chen J, Cao M, Kumar S, Agarwal K, Kumari S (2024) Efficient and privacy-enhanced asynchronous federated learning for multimedia data in edge-based iot. ACM Trans Multimed Comput Commun Appl","DOI":"10.1145\/3688002"},{"key":"1453_CR3","doi-asserted-by":"crossref","unstructured":"Wan Y, Q, Y, Ni W, Xiang Y, Gao L, Hossain E (2024) Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey. IEEE Commun Surv Tutor 26(3):1861\u20131897","DOI":"10.1109\/COMST.2024.3361451"},{"key":"1453_CR4","doi-asserted-by":"crossref","unstructured":"Qu Y, Uddin MP, Gan C, Xiang Y, Gao L, Yearwood J (2022) Blockchain-enabled federated learning: A survey. ACM Comput Surv 55(4):1\u201335","DOI":"10.1145\/3524104"},{"key":"1453_CR5","doi-asserted-by":"crossref","unstructured":"Xu C, Qu Y, Xiang Y, Gao L (2023) Asynchronous federated learning on heterogeneous devices: A survey. Comput Sci Rev 50:100595","DOI":"10.1016\/j.cosrev.2023.100595"},{"key":"1453_CR6","unstructured":"Kone\u010dn\u1ef3 J, McMahan HB, Yu FX, Richt\u00e1rik P, Suresh AT, Bacon D (2016)Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492"},{"key":"1453_CR7","unstructured":"Kone\u010dn\u1ef3 J, McMahan HB, Ramage D, Richt\u00e1rik P (2016) Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527"},{"key":"1453_CR8","doi-asserted-by":"crossref","unstructured":"Shen J, Zhou W, Liu N, Sun H, Li D, Zhang Y (2022) An anchor-free lightweight deep convolutional network for vehicle detection in aerial images. IEEE Trans Intell Transp Syst 23(12):24330\u201324342","DOI":"10.1109\/TITS.2022.3203715"},{"key":"1453_CR9","doi-asserted-by":"crossref","unstructured":"Shen J, Liu N, Xu C, Sun H, Xiao Y, Li D, Zhang Y (2021) Finger vein recognition algorithm based on lightweight deep convolutional neural network. IEEE Trans Instrum Meas 71:1\u201313","DOI":"10.1109\/TIM.2021.3132332"},{"key":"1453_CR10","doi-asserted-by":"crossref","unstructured":"Shen J, Liu N, Sun H, Li D, Zhang Y (2024) An instrument indication acquisition algorithm based on lightweight deep convolutional neural network and hybrid attention fine-grained features. IEEE Trans Instrum Meas 73:1\u201316","DOI":"10.1109\/TIM.2023.3346488"},{"key":"1453_CR11","doi-asserted-by":"crossref","unstructured":"Shenaj, D., Rizzoli, G., Zanuttigh, P.: Federated learning in computer vision. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3310400"},{"issue":"1\u20132","key":"1453_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"P Kairouz","year":"2021","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R et al (2021) Advances and open problems in federated learning. Found Trends Mach Learn 14(1\u20132):1\u2013210","journal-title":"Found Trends Mach Learn"},{"key":"1453_CR13","unstructured":"Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. arXiv preprint arXiv:1806.00582"},{"key":"1453_CR14","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2020) Federated optimization in heterogeneous networks. Proc Mach Learn Syst 2:429\u2013450","journal-title":"Proc Mach Learn Syst"},{"key":"1453_CR15","doi-asserted-by":"crossref","unstructured":"Li Q, Diao Y, Chen Q, He B (2022) Federated learning on non-iid data silos: an experimental study. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 965\u2013978 . IEEE","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"1453_CR16","doi-asserted-by":"crossref","unstructured":"Dai Y, Chen Z, Li J, Heinecke S, Sun L, Xu R (2023) Tackling data heterogeneity in federated learning with class prototypes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 7314\u20137322","DOI":"10.1609\/aaai.v37i6.25891"},{"key":"1453_CR17","unstructured":"Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) Scaffold: Stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132\u20135143. PMLR"},{"key":"1453_CR18","doi-asserted-by":"crossref","unstructured":"Huang W, Ye M, Du B (2022) Learn from others and be yourself in heterogeneous federated learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10143\u201310153","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"1453_CR19","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR"},{"key":"1453_CR20","doi-asserted-by":"crossref","unstructured":"Chen X, He K (2021) Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750\u201315758","DOI":"10.1109\/CVPR46437.2021.01549"},{"issue":"1","key":"1453_CR21","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.patcog.2011.06.019","volume":"45","author":"JG Moreno-Torres","year":"2012","unstructured":"Moreno-Torres JG, Raeder T, Alaiz-Rodr\u00edguez R, Chawla NV, Herrera F (2012) A unifying view on dataset shift in classification. Pattern Recogn 45(1):521\u2013530","journal-title":"Pattern Recogn"},{"key":"1453_CR22","first-page":"5972","volume":"34","author":"M Luo","year":"2021","unstructured":"Luo M, Chen F, Hu D, Zhang Y, Liang J, Feng J (2021) No fear of heterogeneity: classifier calibration for federated learning with non-iid data. Adv Neural Inf Process Syst 34:5972\u20135984","journal-title":"Adv Neural Inf Process Syst"},{"key":"1453_CR23","unstructured":"Zhu Z, Hong J, Zhou J (2021) Data-free knowledge distillation for heterogeneous federated learning. In: International Conference on Machine Learning, pp. 12878\u201312889. PMLR"},{"key":"1453_CR24","doi-asserted-by":"crossref","unstructured":"Hao W, El-Khamy M, Lee J, Zhang J, Liang KJ, Chen C, Duke LC (2021) Towards fair federated learning with zero-shot data augmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3310\u20133319","DOI":"10.1109\/CVPRW53098.2021.00369"},{"key":"1453_CR25","unstructured":"Tang Z, Zhang Y, Shi S, He X, Han B, Chu X (2022) Virtual homogeneity learning: Defending against data heterogeneity in federated learning. In: International Conference on Machine Learning, pp. 21111\u201321132. PMLR"},{"key":"1453_CR26","unstructured":"Jeong E, Oh S, Kim H, Park J, Bennis M, Kim S-L (2018) Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. arXiv preprint arXiv:1811.11479"},{"key":"1453_CR27","doi-asserted-by":"crossref","unstructured":"Luo B, Xiao W, Wang S, Huang J, Tassiulas L (2022) Tackling system and statistical heterogeneity for federated learning with adaptive client sampling. In: IEEE INFOCOM 2022-IEEE Conference on Computer Communications, pp. 1739\u20131748. IEEE","DOI":"10.1109\/INFOCOM48880.2022.9796935"},{"key":"1453_CR28","doi-asserted-by":"crossref","unstructured":"Li C, Zeng X, Zhang M, Cao Z (2022) Pyramidfl: a fine-grained client selection framework for efficient federated learning. In: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking, pp. 158\u2013171","DOI":"10.1145\/3495243.3517017"},{"key":"1453_CR29","first-page":"19586","volume":"33","author":"A Ghosh","year":"2020","unstructured":"Ghosh A, Chung J, Yin D, Ramchandran K (2020) An efficient framework for clustered federated learning. Adv Neural Inf Process Syst 33:19586\u201319597","journal-title":"Adv Neural Inf Process Syst"},{"key":"1453_CR30","doi-asserted-by":"crossref","unstructured":"Zeng S, Li Z, Yu H, He Y, Xu Z, Niyato D, Yu H (2022) Heterogeneous federated learning via grouped sequential-to-parallel training. In: International Conference on Database Systems for Advanced Applications, pp. 455\u2013471. Springer","DOI":"10.1007\/978-3-031-00126-0_34"},{"key":"1453_CR31","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst 30"},{"key":"1453_CR32","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27"},{"key":"1453_CR33","doi-asserted-by":"crossref","unstructured":"Tan Y, Long G, Liu L, Zhou T, Lu Q, Jiang J, Zhang C (2022) Fedproto: federated prototype learning across heterogeneous clients. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8432\u20138440","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"1453_CR34","unstructured":"Wang L, Zhang Q, Sang L, Wu Q, Xu M (2024) Federated prototype-based contrastive learning for privacy-preserving cross-domain recommendation. arXiv preprint arXiv:2409.03294"},{"key":"1453_CR35","doi-asserted-by":"crossref","unstructured":"Huang W, Ye M, Shi Z, Li H, Du B (2023) Rethinking federated learning with domain shift: A prototype view. in 2023 IEEE. In: CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16312\u201316322","DOI":"10.1109\/CVPR52729.2023.01565"},{"issue":"3","key":"1453_CR36","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1007\/s10044-021-00986-w","volume":"24","author":"N Jiang","year":"2021","unstructured":"Jiang N, Fang J, Xu J, Shao Y (2021) Ssd based on contour-material level for domain adaptation. Pattern Anal Appl 24(3):1221\u20131229","journal-title":"Pattern Anal Appl"},{"key":"1453_CR37","unstructured":"Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images"},{"key":"1453_CR38","unstructured":"Caldas S, Duddu SMK, Wu P, Li T, Kone\u010dn\u1ef3 J, McMahan HB, Smith V, Talwalkar A (2018) Leaf: a benchmark for federated settings. arXiv preprint arXiv:1812.01097"},{"key":"1453_CR39","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR"},{"issue":"4","key":"1453_CR40","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s10044-024-01350-4","volume":"27","author":"Y Song","year":"2024","unstructured":"Song Y, Liu H, Zhao S, Jin H, Yu J, Liu Y, Zhai R, Wang L (2024) Fedadkd: heterogeneous federated learning via adaptive knowledge distillation. Pattern Anal Appl 27(4):134","journal-title":"Pattern Anal Appl"},{"key":"1453_CR41","first-page":"38461","volume":"35","author":"G Lee","year":"2022","unstructured":"Lee G, Jeong M, Shin Y, Bae S, Yun S-Y (2022) Preservation of the global knowledge by not-true distillation in federated learning. Adv Neural Inf Process Syst 35:38461\u201338474","journal-title":"Adv Neural Inf Process Syst"},{"key":"1453_CR42","doi-asserted-by":"crossref","unstructured":"Yu F, Zhang W, Qin Z, Xu Z, Wang D, Liu C, Tian Z, Chen X (2021) Fed2: feature-aligned federated learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2066\u20132074","DOI":"10.1145\/3447548.3467309"},{"key":"1453_CR43","doi-asserted-by":"crossref","unstructured":"Li Q, He B, Song D (2021) Model-contrastive federated learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10713\u201310722","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"1453_CR44","doi-asserted-by":"crossref","unstructured":"Li Z, Shang X, He R, Lin T, Wu C (2023) No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5319\u20135329","DOI":"10.1109\/ICCV51070.2023.00490"},{"key":"1453_CR45","unstructured":"Chang H, Shejwalkar V, Shokri R, Houmansadr A (2019) Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer. arXiv preprint arXiv:1912.11279"},{"issue":"24","key":"1453_CR46","doi-asserted-by":"publisher","first-page":"25506","DOI":"10.1109\/JIOT.2022.3197317","volume":"9","author":"E Seo","year":"2022","unstructured":"Seo E, Niyato D, Elmroth E (2022) Resource-efficient federated learning with non-iid data: an auction theoretic approach. IEEE Internet Things J 9(24):25506\u201325524","journal-title":"IEEE Internet Things J"},{"key":"1453_CR47","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110\u20138119","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"1453_CR48","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11)"},{"key":"1453_CR49","doi-asserted-by":"publisher","unstructured":"Yang H-M, Zhang X-Y, Yin F, Liu C-L (2018) Robust classification with convolutional prototype learning. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3474\u20133482. https:\/\/doi.org\/10.1109\/CVPR.2018.00366","DOI":"10.1109\/CVPR.2018.00366"},{"issue":"10","key":"1453_CR50","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1453_CR51","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. Adv Neural Inf Process Syst 33:18661\u201318673","journal-title":"Adv Neural Inf Process Syst"},{"key":"1453_CR52","unstructured":"Hsu T-MH, Qi H, Brown M (2019) Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335"},{"key":"1453_CR53","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1453_CR54","doi-asserted-by":"crossref","unstructured":"Wang Z, Kuang W, Xie Y, Yao L, Li Y, Ding B, Zhou J (2022) Federatedscope-gnn: Towards a unified, comprehensive and efficient package for federated graph learning. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4110\u20134120","DOI":"10.1145\/3534678.3539112"},{"key":"1453_CR55","unstructured":"Allen K, Shelhamer E, Shin H, Tenenbaum J (2019) Infinite mixture prototypes for few-shot learning. In: International Conference on Machine Learning, pp. 232\u2013241. PMLR"},{"key":"1453_CR56","unstructured":"Iandola FN (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and$$<$$ 0.5 mb model size. arXiv preprint arXiv:1602.07360"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01453-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-025-01453-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01453-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:39:57Z","timestamp":1751474397000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-025-01453-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,6]]},"references-count":56,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1453"],"URL":"https:\/\/doi.org\/10.1007\/s10044-025-01453-6","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"type":"print","value":"1433-7541"},{"type":"electronic","value":"1433-755X"}],"subject":[],"published":{"date-parts":[[2025,5,6]]},"assertion":[{"value":"12 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"100"}}