{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T02:13:56Z","timestamp":1768443236017,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Natural Science Foundation of China - Theory and method of cloud manufacturing service process customization for multi-process collaboration under the aggregation service model","award":["No.61973180"],"award-info":[{"award-number":["No.61973180"]}]},{"name":"National Natural Science Foundation of China - Theory and method of cloud manufacturing service process customization for multi-process collaboration under the aggregation service model","award":["No.61973180"],"award-info":[{"award-number":["No.61973180"]}]},{"name":"National Natural Science Foundation of China - Theory and method of cloud manufacturing service process customization for multi-process collaboration under the aggregation service model","award":["No.61973180"],"award-info":[{"award-number":["No.61973180"]}]},{"name":"National Natural Science Foundation of China - Theory and method of cloud manufacturing service process customization for multi-process collaboration under the aggregation service model","award":["No.61973180"],"award-info":[{"award-number":["No.61973180"]}]},{"name":"National Natural Science Foundation of China - Theory and method of cloud manufacturing service process customization for multi-process collaboration under the aggregation service model","award":["No.61973180"],"award-info":[{"award-number":["No.61973180"]}]},{"name":"National Natural Science Foundation of China - Theory and method of cloud manufacturing service process customization for multi-process collaboration under the aggregation service model","award":["No.61973180"],"award-info":[{"award-number":["No.61973180"]}]},{"name":"Theory and method of software crowdsourcing developer recommendation in collaborative open source knowledge sharing communities","award":["No.62172249"],"award-info":[{"award-number":["No.62172249"]}]},{"name":"Theory and method of software crowdsourcing developer recommendation in collaborative open source knowledge sharing communities","award":["No.62172249"],"award-info":[{"award-number":["No.62172249"]}]},{"name":"Theory and method of software crowdsourcing developer recommendation in collaborative open source knowledge sharing communities","award":["No.62172249"],"award-info":[{"award-number":["No.62172249"]}]},{"name":"Theory and method of software crowdsourcing developer recommendation in collaborative open source knowledge sharing communities","award":["No.62172249"],"award-info":[{"award-number":["No.62172249"]}]},{"name":"Theory and method of software crowdsourcing developer recommendation in collaborative open source knowledge sharing communities","award":["No.62172249"],"award-info":[{"award-number":["No.62172249"]}]},{"name":"Theory and method of software crowdsourcing developer recommendation in collaborative open source knowledge sharing communities","award":["No.62172249"],"award-info":[{"award-number":["No.62172249"]}]},{"name":"Shandong Province Industry-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["No.2020-19"],"award-info":[{"award-number":["No.2020-19"]}]},{"name":"Shandong Province Industry-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["No.2020-19"],"award-info":[{"award-number":["No.2020-19"]}]},{"name":"Shandong Province Industry-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["No.2020-19"],"award-info":[{"award-number":["No.2020-19"]}]},{"name":"Shandong Province Industry-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["No.2020-19"],"award-info":[{"award-number":["No.2020-19"]}]},{"name":"Shandong Province Industry-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["No.2020-19"],"award-info":[{"award-number":["No.2020-19"]}]},{"name":"Shandong Province Industry-Education Integration Postgraduate Joint Training Demonstration Base Project","award":["No.2020-19"],"award-info":[{"award-number":["No.2020-19"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07449-7","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T08:54:19Z","timestamp":1752569659000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-stage federated learning with group-wise bidirectional guidance"],"prefix":"10.1007","volume":"81","author":[{"given":"Xiaohui","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taicheng","family":"Bian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dehan","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongtao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"issue":"3","key":"7449_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3501296","volume":"55","author":"DC Nguyen","year":"2022","unstructured":"Nguyen DC, Pham Q-V, Pathirana PN, Ding M, Seneviratne A, Lin Z, Dobre O, Hwang W-J (2022) Federated learning for smart healthcare: a survey. ACM Comput Surv (Csur) 55(3):1\u201337","journal-title":"ACM Comput Surv (Csur)"},{"key":"7449_CR2","volume":"14","author":"A Imteaj","year":"2022","unstructured":"Imteaj A, Amini MH (2022) Leveraging asynchronous federated learning to predict customers financial distress. Intell Syst with Appl 14:200064","journal-title":"Intell Syst with Appl"},{"key":"7449_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854","volume":"149","author":"L Li","year":"2020","unstructured":"Li L, Fan Y, Tse M, Lin K-Y (2020) A review of applications in federated learning. Comput Ind Eng 149:106854","journal-title":"Comput Ind Eng"},{"key":"7449_CR4","doi-asserted-by":"crossref","unstructured":"Lyu L, Yu H, Yang Q (2020) Threats to federated learning: a survey. arXiv preprint arXiv:2003.02133","DOI":"10.1007\/978-3-030-63076-8_1"},{"issue":"6","key":"7449_CR5","doi-asserted-by":"publisher","first-page":"3452","DOI":"10.1109\/TCOMM.2020.2979149","volume":"68","author":"F Ang","year":"2020","unstructured":"Ang F, Chen L, Zhao N, Chen Y, Wang W, Yu FR (2020) Robust federated learning with noisy communication. IEEE Trans Commun 68(6):3452\u20133464","journal-title":"IEEE Trans Commun"},{"key":"7449_CR6","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":"7449_CR7","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/978-3-030-63076-8_8","volume-title":"Federated learning","author":"Y Chen","year":"2020","unstructured":"Chen Y, Yang X, Qin X, Yu H, Chan P, Shen Z (2020) Dealing with label quality disparity in federated learning. In: Yang Q, Fan L, Yu H (eds) Federated learning. Springer, Cham, pp 108\u2013121"},{"key":"7449_CR8","doi-asserted-by":"crossref","unstructured":"Tolpegin V, Truex S, Gursoy ME, Liu L (2020) Data poisoning attacks against federated learning systems. In: Computer security\u2013ESORICs 2020: 25th European Symposium on Research in Computer Security, ESORICs 2020, Guildford, UK, September 14\u201318, 2020, Proceedings, Part i 25, pp. 480\u2013501. Springer","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"7449_CR9","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"},{"key":"7449_CR10","unstructured":"Bagdasaryan E, Veit A, Hua Y, Estrin D, Shmatikov V (2020) How to backdoor federated learning. In: International Conference on Artificial Intelligence and Statistics, pp. 2938\u20132948. PMLR"},{"key":"7449_CR11","unstructured":"Bhagoji AN, Chakraborty S, Mittal P, Calo S (2019) Analyzing federated learning through an adversarial lens. In: International Conference on Machine Learning, pp. 634\u2013643. PMLR"},{"key":"7449_CR12","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":"7449_CR13","unstructured":"Shafahi A, Huang WR, Najibi M, Suciu O, Studer C, Dumitras T, Goldstein T (2018) Poison frogs! targeted clean-label poisoning attacks on neural networks. Adv Neural Inf Process Syst 31"},{"key":"7449_CR14","doi-asserted-by":"crossref","unstructured":"Liu J, Wu J, Chen J, Hu M, Zhou Y, Wu D (2023) Feddwa: personalized federated learning with dynamic weight adjustment. arXiv preprint arXiv:2305.06124","DOI":"10.24963\/ijcai.2023\/444"},{"key":"7449_CR15","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1109\/TSP.2022.3153135","volume":"70","author":"K Pillutla","year":"2022","unstructured":"Pillutla K, Kakade SM, Harchaoui Z (2022) Robust aggregation for federated learning. IEEE Trans Signal Process 70:1142\u20131154","journal-title":"IEEE Trans Signal Process"},{"key":"7449_CR16","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1109\/TIFS.2023.3249568","volume":"18","author":"Y Jiang","year":"2023","unstructured":"Jiang Y, Zhang W, Chen Y (2023) Data quality detection mechanism against label flipping attacks in federated learning. IEEE Trans Inf Forensics Secur 18:1625\u20131637","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"7449_CR17","unstructured":"Yu F, Rawat AS, Menon A, Kumar S (2020) Federated learning with only positive labels. In: International Conference on Machine Learning, pp. 10946\u201310956. PMLR"},{"key":"7449_CR18","doi-asserted-by":"crossref","unstructured":"Gupta A, Luo T, Ngo MV, Das SK (2022) Long-short history of gradients is all you need: detecting malicious and unreliable clients in federated learning. In: European Symposium on Research in Computer Security, pp. 445\u2013465. Springer","DOI":"10.1007\/978-3-031-17143-7_22"},{"key":"7449_CR19","unstructured":"Han B, Yao Q, Yu X, Niu G, Xu M, Hu W, Tsang I, Sugiyama M (2018) Co-teaching: robust training of deep neural networks with extremely noisy labels. Adv Neural Inf Process Syst 31"},{"key":"7449_CR20","unstructured":"Li J, Socher R, Hoi SC (2020) Dividemix: learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394"},{"key":"7449_CR21","unstructured":"Blanchard P, El Mhamdi EM, Guerraoui R, Stainer J (2017) Machine learning with adversaries: byzantine tolerant gradient descent. Adv Neural Inf Process Syst 30"},{"issue":"7","key":"7449_CR22","doi-asserted-by":"publisher","first-page":"1932","DOI":"10.1109\/TMI.2022.3233574","volume":"42","author":"R Yan","year":"2023","unstructured":"Yan R, Qu L, Wei Q, Huang S-C, Shen L, Rubin DL, Xing L, Zhou Y (2023) Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Trans Med Imaging 42(7):1932\u20131943","journal-title":"IEEE Trans Med Imaging"},{"key":"7449_CR23","unstructured":"Zizzo G, Rawat A, Sinn M, Buesser B (2020) Fat: federated adversarial training. arXiv preprint arXiv:2012.01791"},{"key":"7449_CR24","doi-asserted-by":"crossref","unstructured":"Lu Y, Chen L, Zhang Y, Zhang Y, Han B, Cheung Y, Wang H (2024) Federated learning with extremely noisy clients via negative distillation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 14184\u201314192","DOI":"10.1609\/aaai.v38i13.29329"},{"key":"7449_CR25","first-page":"2351","volume":"33","author":"T Lin","year":"2020","unstructured":"Lin T, Kong L, Stich SU, Jaggi M (2020) Ensemble distillation for robust model fusion in federated learning. Adv Neural Inf Process Syst 33:2351\u20132363","journal-title":"Adv Neural Inf Process Syst"},{"key":"7449_CR26","doi-asserted-by":"crossref","unstructured":"Han S, Park S, Wu F, Kim S, Wu C, Xie X, Cha M (2022) Fedx: unsupervised federated learning with cross knowledge distillation. In: European Conference on Computer Vision, pp. 691\u2013707. Springer","DOI":"10.1007\/978-3-031-20056-4_40"},{"key":"7449_CR27","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR"},{"key":"7449_CR28","unstructured":"Krizhevsky A, Hinton G, et al (2009) Learning multiple layers of features from tiny images"},{"key":"7449_CR29","unstructured":"Yurochkin M, Agarwal M, Ghosh S, Greenewald K, Hoang N, Khazaeni Y (2019) Bayesian nonparametric federated learning of neural networks. In: International Conference on Machine Learning, pp. 7252\u20137261. PMLR"},{"key":"7449_CR30","unstructured":"Chen H-Y, Chao W-L (2020) Fedbe: making bayesian model ensemble applicable to federated learning. arXiv preprint arXiv:2009.01974"},{"key":"7449_CR31","doi-asserted-by":"crossref","unstructured":"Uprety A, Rawat DB (2021) Mitigating poisoning attack in federated learning. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 01\u201307. IEEE","DOI":"10.1109\/SSCI50451.2021.9659839"},{"key":"7449_CR32","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"},{"issue":"2","key":"7449_CR33","first-page":"841","volume":"17","author":"S Dangi","year":"2025","unstructured":"Dangi S, Kumar D, Khurana V (2025) Baao: Bayesian and adam optimizer for fault prediction in self-driving software systems using deep learning-based hyperparameter tuning. Int J Inf Technol 17(2):841\u2013850","journal-title":"Int J Inf Technol"},{"key":"7449_CR34","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. Proceed Machi Learn Syst 2:429\u2013450","journal-title":"Proceed Machi Learn Syst"},{"key":"7449_CR35","unstructured":"Huang X, Li P, Li X (2023) Stochastic controlled averaging for federated learning with communication compression. arXiv preprint arXiv:2308.08165"},{"key":"7449_CR36","doi-asserted-by":"crossref","unstructured":"Wu N, Yu L, Jiang X, Cheng K-T, Yan Z (2023) Fednoro: towards noise-robust federated learning by addressing class imbalance and label noise heterogeneity. arXiv preprint arXiv:2305.05230","DOI":"10.24963\/ijcai.2023\/492"},{"key":"7449_CR37","doi-asserted-by":"crossref","unstructured":"Xu J, Chen Z, Quek TQ, Chong KFE (2022) Fedcorr: multi-stage federated learning for label noise correction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10184\u201310193","DOI":"10.1109\/CVPR52688.2022.00994"},{"key":"7449_CR38","doi-asserted-by":"crossref","unstructured":"Lycklama H, Burkhalter L, Viand A, K\u00fcchler N, Hithnawi A (2023) Rofl: robustness of secure federated learning. In: 2023 IEEE Symposium on Security and Privacy (SP), pp. 453\u2013476. IEEE","DOI":"10.1109\/SP46215.2023.10179400"},{"key":"7449_CR39","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1016\/j.future.2022.12.003","volume":"141","author":"E Isik-Polat","year":"2023","unstructured":"Isik-Polat E, Polat G, Kocyigit A (2023) Arfed: Attack-resistant federated averaging based on outlier elimination. Futur Gener Comput Syst 141:626\u2013650","journal-title":"Futur Gener Comput Syst"},{"key":"7449_CR40","doi-asserted-by":"crossref","unstructured":"Jebreel N, Blanco-Justicia A, S\u00e1nchez D, Domingo-Ferrer J (2020) Efficient detection of byzantine attacks in federated learning using last layer biases. In: Modeling Decisions for Artificial Intelligence: 17th International Conference, MDAI 2020, Sant Cugat, Spain, September 2\u20134, 2020, Proceedings 17, pp. 154\u2013165. Springer","DOI":"10.1007\/978-3-030-57524-3_13"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07449-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07449-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07449-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T10:43:10Z","timestamp":1757241790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07449-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"references-count":40,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["7449"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07449-7","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,15]]},"assertion":[{"value":"12 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1157"}}