{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:32:28Z","timestamp":1779294748862,"version":"3.51.4"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["XJCX2023013"],"award-info":[{"award-number":["XJCX2023013"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002369"],"award-info":[{"award-number":["62002369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"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\/s13042-025-02794-8","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T01:35:54Z","timestamp":1757640954000},"page":"10403-10420","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-task federated learning with encoder\u2013decoder structure: enabling collaborative learning across different tasks"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0898-7797","authenticated-orcid":false,"given":"Jingxuan","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1867-3660","authenticated-orcid":false,"given":"Weidong","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4199-2793","authenticated-orcid":false,"given":"Ji","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dayu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiongtao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaohong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"2794_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/B978-0-12-802581-9.00001-9","volume-title":"Medical image recognition, segmentation and parsing. The Elsevier and MICCAI Society Book Series","author":"SK Zhou","year":"2016","unstructured":"Zhou SK (2016) Chapter 1\u2013introduction to medical image recognition, segmentation, and parsing. In: Zhou SK (ed) Medical image recognition, segmentation and parsing. The Elsevier and MICCAI Society Book Series. Academic Press, pp 1\u201321. https:\/\/doi.org\/10.1016\/B978-0-12-802581-9.00001-9"},{"key":"2794_CR2","unstructured":"Vandenhende S (2022) Multi-task learning for visual scene understanding. PhD thesis, KU Leuven"},{"key":"2794_CR3","unstructured":"Kone\u010dn\u00fd J, McMahan B, Ramage D (2015) Federated optimization: distributed optimization beyond the datacenter. arXiv e-prints, 1511\u201303575 arXiv:1511.03575 [cs.LG]"},{"key":"2794_CR4","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"2794_CR5","volume-title":"Advances in neural information processing systems","author":"V Smith","year":"2017","unstructured":"Smith V, Chiang C-K, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30. Curran Associates Inc"},{"key":"2794_CR6","first-page":"41","volume-title":"Advances in neural information processing systems","author":"A Argyriou","year":"2006","unstructured":"Argyriou A, Evgeniou T, Pontil M (2006) Multi-task feature learning. In: Sch\u00f6lkopf B, Platt J, Hoffman T (eds) Advances in neural information processing systems, vol 19. MIT Press, pp 41\u201348"},{"key":"2794_CR7","doi-asserted-by":"publisher","unstructured":"Chen J, Zhou J, Ye J (2011) Integrating low-rank and group-sparse structures for robust multi-task learning. In: Proceedings of the 17th ACM SIGKDD International Conference on knowledge discovery and data mining. KDD \u201911, pp. 42\u201350. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2020408.2020423","DOI":"10.1145\/2020408.2020423"},{"issue":"1","key":"2794_CR8","first-page":"1205","volume":"17","author":"AR Gon\u00e7alves","year":"2016","unstructured":"Gon\u00e7alves AR, Von Zuben FJ, Banerjee A (2016) Multi-task sparse structure learning with Gaussian copula models. J Mach Learn Res 17(1):1205\u20131234","journal-title":"J Mach Learn Res"},{"key":"2794_CR9","doi-asserted-by":"publisher","unstructured":"Evgeniou T, Pontil M (2004) Regularized multi\u2013task learning. In: Proceedings of the Tenth ACM SIGKDD International Conference on knowledge discovery and data mining. KDD \u201904, pp. 109\u2013117. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/1014052.1014067","DOI":"10.1145\/1014052.1014067"},{"key":"2794_CR10","unstructured":"Jacob L, Bach F, Vert JP (2008) Clustered multi-task learning: a convex formulation. In: Proceedings of the 21st International Conference on neural information processing systems. NIPS\u201908, pp. 745\u2013752. Curran Associates Inc., Red Hook, NY, USA"},{"issue":"8","key":"2794_CR11","doi-asserted-by":"publisher","first-page":"1000587","DOI":"10.1371\/journal.pgen.1000587","volume":"5","author":"S Kim","year":"2009","unstructured":"Kim S, Xing EP (2009) Statistical estimation of correlated genome associations to a quantitative trait network. PLoS Genet 5(8):1000587. https:\/\/doi.org\/10.1371\/journal.pgen.1000587","journal-title":"PLoS Genet"},{"key":"2794_CR12","doi-asserted-by":"publisher","unstructured":"Zhang Y, Yeung DY (2010) A convex formulation for learning task relationships in multi-task learning. In: Proceedings of the Twenty-Sixth Conference on uncertainty in artificial intelligence. UAI\u201910, pp. 733\u2013742. AUAI Press, Arlington, Virginia, USA. https:\/\/doi.org\/10.5555\/3023549.3023636","DOI":"10.5555\/3023549.3023636"},{"issue":"1","key":"2794_CR13","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1093\/nsr\/nwx105","volume":"5","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Yang Q (2017) An overview of multi-task learning. Natl Sci Rev 5(1):30\u201343. https:\/\/doi.org\/10.1093\/nsr\/nwx105 (https:\/\/arxiv.org\/abs\/academic.oup.com\/nsr\/article-pdf\/5\/1\/30\/31567358\/nwx105.pdf)","journal-title":"Natl Sci Rev"},{"issue":"1","key":"2794_CR14","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/a:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375. https:\/\/doi.org\/10.1023\/a:1007379606734","journal-title":"Mach Learn"},{"key":"2794_CR15","doi-asserted-by":"crossref","unstructured":"Xu D, Ouyang W, Wang X, Sebe N (2018) Pad-net: multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing. In: Proceedings of the IEEE Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00077"},{"key":"2794_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126284","volume":"548","author":"H Ran","year":"2023","unstructured":"Ran H, Ning X, Li W, Hao M, Tiwari P (2023) 3d human pose and shape estimation via de-occlusion multi-task learning. Neurocomputing 548:126284. https:\/\/doi.org\/10.1016\/j.neucom.2023.126284","journal-title":"Neurocomputing"},{"issue":"12","key":"2794_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2021.3070203","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Yang Q (2021) A survey on multi-task learning. IEEE Trans Knowl Data Eng 34(12):1\u20131. https:\/\/doi.org\/10.1109\/TKDE.2021.3070203","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"2794_CR18","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1109\/TNNLS.2020.3028022","volume":"32","author":"F Zhou","year":"2021","unstructured":"Zhou F, Shui C, Abbasi M, Robitaille L\u00c9, Wang B, Gagn\u00e9 C (2021) Task similarity estimation through adversarial multitask neural network. IEEE Trans Neural Netw Learn Syst 32(2):466\u2013480. https:\/\/doi.org\/10.1109\/TNNLS.2020.3028022","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2794_CR19","unstructured":"Brendan McMahan H, Moore E, Ramage D, Hampson S, Ag\u00fcera y Arcas B (2016) Communication-efficient learning of deep networks from decentralized data. arXiv e-prints, 1602\u201305629 arXiv:1602.05629 [cs.LG]"},{"key":"2794_CR20","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":"2794_CR21","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":"2794_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126407","volume":"548","author":"L Song","year":"2023","unstructured":"Song L, Wang H, Zhang G, Yu S (2023) Fedinf: social influence prediction with federated learning. Neurocomputing 548:126407. https:\/\/doi.org\/10.1016\/j.neucom.2023.126407","journal-title":"Neurocomputing"},{"key":"2794_CR23","doi-asserted-by":"crossref","unstructured":"Wang J, Zhang J, Bao W, Zhu X, Cao B, Yu PS (2018) Not just privacy: improving performance of private deep learning in mobile cloud. In: Proceedings of the 24th ACM SIGKDD International Conference on knowledge discovery & data mining, pp. 2407\u20132416","DOI":"10.1145\/3219819.3220106"},{"key":"2794_CR24","doi-asserted-by":"publisher","first-page":"2529","DOI":"10.1007\/s11280-020-00780-4","volume":"23","author":"X Huang","year":"2020","unstructured":"Huang X, Ding Y, Jiang ZL, Qi S, Wang X, Liao Q (2020) Dp-fl: a novel differentially private federated learning framework for the unbalanced data. World Wide Web 23:2529\u20132545","journal-title":"World Wide Web"},{"key":"2794_CR25","doi-asserted-by":"crossref","unstructured":"Majeed U, Hong CS (2019) Flchain: federated learning via mec-enabled blockchain network. In: 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1\u20134. IEEE","DOI":"10.23919\/APNOMS.2019.8892848"},{"issue":"6","key":"2794_CR26","doi-asserted-by":"publisher","first-page":"10700","DOI":"10.1109\/JIOT.2019.2940820","volume":"6","author":"J Kang","year":"2019","unstructured":"Kang J, Xiong Z, Niyato D, Xie S, Zhang J (2019) Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J 6(6):10700\u201310714","journal-title":"IEEE Internet Things J"},{"key":"2794_CR27","first-page":"3557","volume":"33","author":"A Fallah","year":"2020","unstructured":"Fallah A, Mokhtari A, Ozdaglar A (2020) Personalized federated learning with theoretical guarantees: a model-agnostic meta-learning approach. Adv Neural Inf Process Syst 33:3557\u20133568","journal-title":"Adv Neural Inf Process Syst"},{"key":"2794_CR28","first-page":"5925","volume":"35","author":"K Liu","year":"2022","unstructured":"Liu K, Hu S, Wu SZ, Smith V (2022) On privacy and personalization in cross-silo federated learning. Adv Neural Inf Process Syst 35:5925\u20135940","journal-title":"Adv Neural Inf Process Syst"},{"key":"2794_CR29","doi-asserted-by":"crossref","unstructured":"He C, Ceyani E, Balasubramanian K, Annavaram M, Avestimehr S (2022) Spreadgnn: Decentralized multi-task federated learning for graph neural networks on molecular data. In: Proceedings of the AAAI Conference on artificial intelligence, vol. 36, pp. 6865\u20136873","DOI":"10.1609\/aaai.v36i6.20643"},{"key":"2794_CR30","unstructured":"Collins L, Hassani H, Mokhtari A, Shakkottai S (2021) Exploiting shared representations for personalized federated learning. In: International Conference on machine learning, pp. 2089\u20132099. PMLR"},{"key":"2794_CR31","unstructured":"Li T, Hu S, Beirami A, Smith V (2021) Ditto: fair and robust federated learning through personalization. In: Meila M, Zhang T (eds) Proceedings of the 38th International Conference on machine learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 6357\u20136368"},{"issue":"6","key":"2794_CR32","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1109\/TPDS.2023.3289444","volume":"35","author":"Z Wu","year":"2024","unstructured":"Wu Z, Sun S, Wang Y, Liu M, Pan Q, Jiang X, Gao B (2024) Fedict: federated multi-task distillation for multi-access edge computing. IEEE Trans Parallel Distrib Syst 35(6):1107\u20131121. https:\/\/doi.org\/10.1109\/TPDS.2023.3289444","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"2794_CR33","doi-asserted-by":"publisher","unstructured":"Chen J, Zhang A (2022) Fedmsplit: Correlation-adaptive federated multi-task learning across multimodal split networks. In: Zhang A, Rangwala H (eds) KDD \u201922: The 28th ACM SIGKDD Conference on knowledge discovery and data mining, Washington, DC, USA, August 14\u201318, 2022, pp. 87\u201396. https:\/\/doi.org\/10.1145\/3534678.3539384","DOI":"10.1145\/3534678.3539384"},{"key":"2794_CR34","unstructured":"Yao L, Gao D, Wang Z, Xie Y, Kuang W, Chen D, Wang H, Dong C, Ding B, Li Y (2022) A benchmark for federated hetero-task learning. ArXiv preprint abs\/2206.03436"},{"key":"2794_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3587913","author":"H Jiang","year":"2025","unstructured":"Jiang H, Cao X, Ouyang T, Lin M, Zhao K, Zhang X, Chen X (2025) Efficient multi-task asynchronous federated learning in edge computing: a two-layer optimization approach. IEEE Internet of Things J. https:\/\/doi.org\/10.1109\/JIOT.2025.3587913","journal-title":"IEEE Internet of Things J"},{"key":"2794_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2025.3571953","author":"R Xie","year":"2025","unstructured":"Xie R, Li C, Yang Z, Xu Z, Huang J, Dong Z (2025) Differential privacy enabled robust asynchronous federated multitask learning: a multigradient descent approach. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2025.3571953","journal-title":"IEEE Trans Cybern"},{"key":"2794_CR37","doi-asserted-by":"crossref","unstructured":"Kokkinos I (2017) Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp. 5454\u20135463","DOI":"10.1109\/CVPR.2017.579"},{"key":"2794_CR38","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick LC (2014) Microsoft coco: common objects in context. In: COMPUTER VISION - ECCV 2014(PT V):740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2794_CR39","doi-asserted-by":"crossref","unstructured":"Mottaghi R, Chen X, Liu X, Cho N-G, Lee S-W, Fidler S, Urtasun R, Yuille A (2014) The role of context for object detection and semantic segmentation in the wild. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 891\u2013898","DOI":"10.1109\/CVPR.2014.119"},{"key":"2794_CR40","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) ECCV. Springer, Cham, pp 833\u2013851","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2794_CR41","doi-asserted-by":"publisher","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: 2017 IEEE International Conference on computer vision (ICCV), pp. 2980\u20132988. https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"2794_CR42","doi-asserted-by":"publisher","unstructured":"Kirillov A, Girshick R, He K, Doll\u00e1r P (2019) Panoptic feature pyramid networks. In: 2019 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 6392\u20136401. https:\/\/doi.org\/10.1109\/CVPR.2019.00656","DOI":"10.1109\/CVPR.2019.00656"},{"key":"2794_CR43","volume-title":"Advances in neural information processing systems","author":"A Newell","year":"2017","unstructured":"Newell A, Huang Z, Deng J (2017) Associative embedding: end-to-end learning for joint detection and grouping. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30. Curran Associates Inc"},{"key":"2794_CR44","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587"},{"issue":"5","key":"2794_CR45","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TPAMI.2004.1273918","volume":"26","author":"DR Martin","year":"2004","unstructured":"Martin DR, Fowlkes CC, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530\u2013549. https:\/\/doi.org\/10.1109\/TPAMI.2004.1273918","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2794_CR46","doi-asserted-by":"publisher","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp. 3431\u20133440. https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"2794_CR47","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2009","unstructured":"Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A (2009) The pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303\u2013338. https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int J Comput Vis"},{"key":"2794_CR48","doi-asserted-by":"publisher","unstructured":"Kirillov A, He K, Girshick R, Rother C, Doll\u00e1r P (2019) Panoptic segmentation. In: 2019 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp. 9396\u20139405. https:\/\/doi.org\/10.1109\/CVPR.2019.00963","DOI":"10.1109\/CVPR.2019.00963"},{"key":"2794_CR49","unstructured":"Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. CoRR abs\/1806.00582 arXiv:1806.00582"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02794-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02794-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02794-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:42:03Z","timestamp":1765618923000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02794-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"references-count":49,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2794"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02794-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,12]]},"assertion":[{"value":"3 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 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 declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}