{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:34:10Z","timestamp":1772120050961,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the National Natural ScienceFoundation of China","award":["62072052"],"award-info":[{"award-number":["62072052"]}]},{"name":"the National Natural ScienceFoundation of China","award":["62072052"],"award-info":[{"award-number":["62072052"]}]},{"name":"the Innovation Research Group Project of NSFC","award":["61921003"],"award-info":[{"award-number":["61921003"]}]},{"name":"the Innovation Research Group Project of NSFC","award":["61921003"],"award-info":[{"award-number":["61921003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s13042-025-02612-1","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:17:29Z","timestamp":1743081449000},"page":"10175-10190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PFDP: privacy-preserving federated distillation method for pretrained language models"],"prefix":"10.1007","volume":"16","author":[{"given":"Chaomeng","family":"Chen","sequence":"first","affiliation":[]},{"given":"Sen","family":"Su","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,25]]},"reference":[{"key":"2612_CR1","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"2612_CR2","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"2612_CR3","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32"},{"key":"2612_CR4","unstructured":"Clark K, Luong M-T, Le QV, Manning CD (2020) Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555"},{"issue":"6","key":"2612_CR5","first-page":"5597","volume":"35","author":"L Sun","year":"2022","unstructured":"Sun L, Zhang Z, Wang F, Ji P, Wen J, Su S, Philip SY (2022) Aligning dynamic social networks: An optimization over dynamic graph autoencoder. IEEE Trans Knowl Data Eng 35(6):5597\u20135611","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2612_CR6","unstructured":"Li P, Yu X, Peng H, Xian Y, Wang L, Sun L, Zhang J, Yu PS. Relational prompt-based pre-trained language models for social event detection. ACM Transactions on Information Systems"},{"issue":"5","key":"2612_CR7","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.clsr.2013.07.010","volume":"29","author":"WB Chik","year":"2013","unstructured":"Chik WB (2013) The singapore personal data protection act and an assessment of future trends in data privacy reform. Computer Law & Security Review 29(5):554\u2013575","journal-title":"Computer Law & Security Review"},{"key":"2612_CR8","doi-asserted-by":"crossref","unstructured":"Ji S, Pan S, Long G, Li X, Jiang J, Huang Z (2019) Learning private neural language modeling with attentive aggregation. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE","DOI":"10.1109\/IJCNN.2019.8852464"},{"issue":"5","key":"2612_CR9","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MIS.2020.3017205","volume":"36","author":"G Lin","year":"2020","unstructured":"Lin G, Liang F, Pan W, Ming Z (2020) Fedrec: Federated recommendation with explicit feedback. IEEE Intell Syst 36(5):21\u201330","journal-title":"IEEE Intell Syst"},{"key":"2612_CR10","unstructured":"Liu D, Miller T (2020) Federated pretraining and fine tuning of bert using clinical notes from multiple silos. arXiv preprint arXiv:2002.08562"},{"key":"2612_CR11","unstructured":"Ge S, Wu F, Wu C, Qi T, Huang Y, Xie X (2020) Fedner: Privacy-preserving medical named entity recognition with federated learning. arXiv preprint arXiv:2003.09288"},{"key":"2612_CR12","doi-asserted-by":"crossref","unstructured":"Sui D, Chen Y, Zhao J, Jia Y, Xie Y, Sun W (2020) Feded: Federated learning via ensemble distillation for medical relation extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2118\u20132128","DOI":"10.18653\/v1\/2020.emnlp-main.165"},{"key":"2612_CR13","doi-asserted-by":"crossref","unstructured":"Lin BY, He C, Zeng Z, Wang H, Huang Y, Dupuy C, Gupta R, Soltanolkotabi M, Ren X, Avestimehr S (2021) Fednlp: Benchmarking federated learning methods for natural language processing tasks. arXiv preprint arXiv:2104.08815","DOI":"10.18653\/v1\/2022.findings-naacl.13"},{"issue":"4","key":"2612_CR14","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1007\/s13042-023-01974-8","volume":"15","author":"L Sun","year":"2024","unstructured":"Sun L, Ye J, Zhang J, Yang Y, Liu M, Wang F, Yu PS (2024) Contrastive sequential interaction network learning on co-evolving riemannian spaces. Int J Mach Learn Cybern 15(4):1397\u20131413","journal-title":"Int J Mach Learn Cybern"},{"key":"2612_CR15","unstructured":"Yang Z, Zhang Y, Zheng Y, Tian X, Peng H, Liu T, Han B (2024) Fedfed: Feature distillation against data heterogeneity in federated learning. Advances in Neural Information Processing Systems 36"},{"key":"2612_CR16","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":"2612_CR17","unstructured":"Li D, Wang J (2019) Fedmd: Heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581"},{"key":"2612_CR18","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"},{"key":"2612_CR19","doi-asserted-by":"publisher","first-page":"11891","DOI":"10.1609\/aaai.v36i11.21446","volume":"36","author":"X Gong","year":"2022","unstructured":"Gong X, Sharma A, Karanam S, Wu Z, Chen T, Doermann D, Innanje A (2022) Preserving privacy in federated learning with ensemble cross-domain knowledge distillation. Proceedings of the AAAI Conference on Artificial Intelligence 36:11891\u201311899","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"1","key":"2612_CR20","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1109\/TMC.2021.3070013","volume":"22","author":"S Itahara","year":"2021","unstructured":"Itahara S, Nishio T, Koda Y, Morikura M, Yamamoto K (2021) Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data. IEEE Trans Mob Comput 22(1):191\u2013205","journal-title":"IEEE Trans Mob Comput"},{"key":"2612_CR21","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.ins.2021.01.046","volume":"560","author":"L Hu","year":"2021","unstructured":"Hu L, Yan H, Li L, Pan Z, Liu X, Zhang Z (2021) Mhat: An efficient model-heterogenous aggregation training scheme for federated learning. Inf Sci 560:493\u2013503","journal-title":"Inf Sci"},{"issue":"3","key":"2612_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3580509","volume":"17","author":"L Sun","year":"2023","unstructured":"Sun L, Du Y, Gao S, Ye J, Wang F, Ren F, Liang M, Wang Y, Wang S (2023) Groupaligner: a deep reinforcement learning with domain adaptation for social group alignment. ACM Trans Web 17(3):1\u201330","journal-title":"ACM Trans Web"},{"key":"2612_CR23","unstructured":"Zhu L, Liu Z, Han S (2019) Deep leakage from gradients. Advances in neural information processing systems 32"},{"key":"2612_CR24","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQ, Poor HV (2020) Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans Inf Forensics Secur 15:3454\u20133469","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2612_CR25","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":"2612_CR26","doi-asserted-by":"publisher","first-page":"7865","DOI":"10.1609\/aaai.v35i9.16960","volume":"35","author":"Y Huang","year":"2021","unstructured":"Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, Zhang Y (2021) Personalized cross-silo federated learning on non-iid data. Proceedings of the AAAI Conference on Artificial Intelligence 35:7865\u20137873","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2612_CR27","unstructured":"Chen C, Su S. A federated graph learning framework for brain connectome"},{"key":"2612_CR28","doi-asserted-by":"crossref","unstructured":"Chen C, Zhou Z, Tang P, He L, Su S (2024) Enforcing group fairness in privacy-preserving federated learning. Future Generation Computer Systems","DOI":"10.2139\/ssrn.4601101"},{"key":"2612_CR29","unstructured":"Wang H, Yurochkin M, Sun Y, Papailiopoulos D, Khazaeni Y (2020) Federated learning with matched averaging. arXiv preprint arXiv:2002.06440"},{"key":"2612_CR30","unstructured":"Deng Y, Kamani MM, Mahdavi M (2020) Adaptive personalized federated learning. arXiv preprint arXiv:2003.13461"},{"key":"2612_CR31","unstructured":"Bhowmick A, Duchi J, Freudiger J, Kapoor G, Rogers R (2018) Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984"},{"key":"2612_CR32","doi-asserted-by":"crossref","unstructured":"Melis L, Song C, De\u00a0Cristofaro E, Shmatikov V (2019) Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 691\u2013706. IEEE","DOI":"10.1109\/SP.2019.00029"},{"key":"2612_CR33","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":"2612_CR34","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":"2612_CR35","doi-asserted-by":"crossref","unstructured":"Ma X, Liu J, Wang J, Zhang X (2023) Fedid: Federated interactive distillation for large-scale pretraining language models. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 8566\u20138577","DOI":"10.18653\/v1\/2023.emnlp-main.529"},{"key":"2612_CR36","unstructured":"Geyer RC, Klein T, Nabi M (2017) Differentially private federated learning: A client level perspective. CoRR abs\/1712.07557 arXiv:1712.07557"},{"key":"2612_CR37","unstructured":"McMahan HB, Ramage D, Talwar K, Zhang L (2018) Learning differentially private recurrent language models. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, pp. 123\u2013132. OpenReview.net, ???. https:\/\/openreview.net\/forum?id=BJ0hF1Z0b"},{"key":"2612_CR38","unstructured":"Naseri M, Hayes J, De\u00a0Cristofaro E (2020) Local and central differential privacy for robustness and privacy in federated learning. arXiv preprint arXiv:2009.03561"},{"key":"2612_CR39","unstructured":"Girgis AM, Data D, Diggavi SN (2021) Renyi differential privacy of the subsampled shuffle model in distributed learning. In: Ranzato, M., Beygelzimer, A., Dauphin, Y.N., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, Virtual, pp. 29181\u201329192. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/f44ec26e2ac3f1ab8c2472d4b1c2ea86-Abstract.html"},{"key":"2612_CR40","doi-asserted-by":"publisher","first-page":"8688","DOI":"10.1609\/aaai.v35i10.17053","volume":"35","author":"R Liu","year":"2021","unstructured":"Liu R, Cao Y, Chen H, Guo R, Yoshikawa M (2021) Flame: Differentially private federated learning in the shuffle model. Proceedings of the AAAI Conference on Artificial Intelligence 35:8688\u20138696","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2612_CR41","doi-asserted-by":"crossref","unstructured":"Cheng A, Wang P, Zhang XS, Cheng J (2022) Differentially private federated learning with local regularization and sparsification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10122\u201310131","DOI":"10.1109\/CVPR52688.2022.00988"},{"key":"2612_CR42","doi-asserted-by":"crossref","unstructured":"Boenisch F, Dziedzic A, Schuster R, Shamsabadi AS, Shumailov I, Papernot N (2023) Reconstructing individual data points in federated learning hardened with differential privacy and secure aggregation. In: 2023 IEEE 8th European Symposium on Security and Privacy (EuroS &P), pp. 241\u2013257. IEEE","DOI":"10.1109\/EuroSP57164.2023.00023"},{"key":"2612_CR43","doi-asserted-by":"crossref","unstructured":"Wei J, Bao E, Xiao X, Yang Y (2022) Dpis: An enhanced mechanism for differentially private sgd with importance sampling. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2885\u20132899","DOI":"10.1145\/3548606.3560562"},{"key":"2612_CR44","unstructured":"Yang Y, Hui B, Yuan H, Gong N, Cao Y (2023) $$privatefl$$: Accurate, differentially private federated learning via personalized data transformation. In: 32nd USENIX Security Symposium (USENIX Security 23), 1595\u20131612"},{"issue":"1","key":"2612_CR45","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376","journal-title":"Proc IEEE"},{"key":"2612_CR46","doi-asserted-by":"crossref","unstructured":"Duan L, Xu D, Chang S-F (2012) Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1338\u20131345. IEEE","DOI":"10.1109\/CVPR.2012.6247819"},{"key":"2612_CR47","doi-asserted-by":"crossref","unstructured":"Yao Y, Doretto G (2010) Boosting for transfer learning with multiple sources. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1855\u20131862. IEEE","DOI":"10.1109\/CVPR.2010.5539857"},{"key":"2612_CR48","doi-asserted-by":"crossref","unstructured":"Huang X, Rao Y, Xie H, Wong T-L, Wang FL (2017) Cross-domain sentiment classification via topic-related tradaboost. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31","DOI":"10.1609\/aaai.v31i1.11099"},{"issue":"24","key":"2612_CR49","doi-asserted-by":"publisher","first-page":"6520","DOI":"10.1109\/TSP.2017.2752695","volume":"65","author":"K Li","year":"2017","unstructured":"Li K, Principe JC (2017) Transfer learning in adaptive filters: The nearest instance centroid-estimation kernel least-mean-square algorithm. IEEE Trans Signal Process 65(24):6520\u20136535","journal-title":"IEEE Trans Signal Process"},{"key":"2612_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.106906","volume":"148","author":"H Ren","year":"2019","unstructured":"Ren H, Liu W, Shan M, Wang X (2019) A new wind turbine health condition monitoring method based on vmd-mpe and feature-based transfer learning. Measurement 148:106906","journal-title":"Measurement"},{"key":"2612_CR51","doi-asserted-by":"crossref","unstructured":"Davis J, Domingos P (2009) Deep transfer via second-order markov logic. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 217\u2013224","DOI":"10.1145\/1553374.1553402"},{"key":"2612_CR52","doi-asserted-by":"crossref","unstructured":"Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308\u2013318","DOI":"10.1145\/2976749.2978318"},{"key":"2612_CR53","doi-asserted-by":"crossref","unstructured":"Friedman A, Schuster A (2010) Data mining with differential privacy. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 493\u2013502","DOI":"10.1145\/1835804.1835868"},{"key":"2612_CR54","doi-asserted-by":"crossref","unstructured":"Dwork C (2006) Differential privacy. In: International Colloquium on Automata, Languages, and Programming, pp. 1\u201312. Springer","DOI":"10.1007\/11787006_1"},{"key":"2612_CR55","unstructured":"Levesque H, Davis E, Morgenstern L (2012) The winograd schema challenge. In: Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning"},{"key":"2612_CR56","doi-asserted-by":"crossref","unstructured":"Dagan I, Glickman O, Magnini B (2005) The pascal recognising textual entailment challenge. In: Machine Learning Challenges Workshop, pp. 177\u2013190. Springer","DOI":"10.1007\/11736790_9"},{"key":"2612_CR57","unstructured":"Dolan B, Brockett, C (2005) Automatically constructing a corpus of sentential paraphrases. In: Third International Workshop on Paraphrasing (IWP2005)"},{"key":"2612_CR58","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1162\/tacl_a_00290","volume":"7","author":"A Warstadt","year":"2019","unstructured":"Warstadt A, Singh A, Bowman SR (2019) Neural network acceptability judgments. Transactions of the Association for Computational Linguistics 7:625\u2013641","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"2612_CR59","doi-asserted-by":"crossref","unstructured":"Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631\u20131642","DOI":"10.18653\/v1\/D13-1170"},{"key":"2612_CR60","doi-asserted-by":"crossref","unstructured":"Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250","DOI":"10.18653\/v1\/D16-1264"},{"key":"2612_CR61","doi-asserted-by":"crossref","unstructured":"Williams A, Nangia N, Bowman SR (2017) A broad-coverage challenge corpus for sentence understanding through inference. arXiv preprint arXiv:1704.05426","DOI":"10.18653\/v1\/N18-1101"},{"key":"2612_CR62","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":"2612_CR63","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02612-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02612-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02612-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:42:02Z","timestamp":1765618922000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02612-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,25]]},"references-count":63,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2612"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02612-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4247440\/v1","asserted-by":"object"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,25]]},"assertion":[{"value":"10 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}