{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T12:22:47Z","timestamp":1772281367820,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"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":["62166023"],"award-info":[{"award-number":["62166023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of the Joint Fund of the National Foundation of China","award":["U24A20334"],"award-info":[{"award-number":["U24A20334"]}]},{"name":"Yunnan provincial major science and technology special plan projects","award":["202302AD080003"],"award-info":[{"award-number":["202302AD080003"]}]},{"name":"Special Program for Basic Research of Yunnan Province","award":["202401BC070021"],"award-info":[{"award-number":["202401BC070021"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13042-025-02895-4","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T11:51:49Z","timestamp":1772279509000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized federated multilingual machine translation with multi-level knowledge fusion"],"prefix":"10.1007","volume":"17","author":[{"given":"Cunli","family":"Mao","sequence":"first","affiliation":[]},{"given":"Yifei","family":"Luan","sequence":"additional","affiliation":[]},{"given":"Zhenhan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yuxin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Shengxiang","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"issue":"5","key":"2895_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3406095","volume":"53","author":"R Dabre","year":"2020","unstructured":"Dabre R, Chu C, Kunchukuttan A (2020) A survey of multilingual neural machine translation. ACM Comput Surv (CSUR) 53(5):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"2895_CR2","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1162\/tacl_a_00065","volume":"5","author":"M Johnson","year":"2017","unstructured":"Johnson M, Schuster M, Le QV et al (2017) Google\u2019s multilingual neural machine translation system: enabling zero-shot translation. Trans Assoc Comput Linguist 5:339\u2013351","journal-title":"Trans Assoc Comput Linguist"},{"key":"2895_CR3","unstructured":"Vilar D, Freitag M, Cherry C, et\u00a0al (2022) Prompting palm for translation: Assessing strategies and performance. arXiv:2211.09102"},{"key":"2895_CR4","unstructured":"Imani A, Lin P, Kargaran AH, et\u00a0al (2023) Glot500: scaling multilingual corpora and language models to 500 languages. arXiv:2305.12182"},{"key":"2895_CR5","unstructured":"Lin P, Ji S, Tiedemann J, et\u00a0al (2024) Mala-500: Massive language adaptation of large language models. arXiv:2401.13303"},{"key":"2895_CR6","unstructured":"Logacheva V, Teslenko D, Shelmanov A, et\u00a0al (2020) Word sense disambiguation for 158 languages using word embeddings only. arXiv:2003.06651"},{"key":"2895_CR7","doi-asserted-by":"crossref","unstructured":"Sui D, Chen Y, Zhao J et al (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":"2895_CR8","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Zaheer M et al (2020) Federated optimization in heterogeneous networks. Proc Mach Learn Syst 2:429\u2013450","journal-title":"Proc Mach Learn Syst"},{"key":"2895_CR9","doi-asserted-by":"crossref","unstructured":"Huang W, Wan G, Ye M, et\u00a0al (2024) Federated graph semantic and structural learning. arXiv:2406.18937","DOI":"10.24963\/ijcai.2023\/426"},{"issue":"12","key":"2895_CR10","doi-asserted-by":"publisher","first-page":"9587","DOI":"10.1109\/TNNLS.2022.3160699","volume":"34","author":"AZ Tan","year":"2022","unstructured":"Tan AZ, Yu H, Cui L et al (2022) Towards personalized federated learning. IEEE Trans Neural Netw Learn Syst 34(12):9587\u20139603","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2895_CR11","first-page":"23309","volume":"34","author":"B Sun","year":"2021","unstructured":"Sun B, Huo H, Yang Y et al (2021) Partialfed: cross-domain personalized federated learning via partial initialization. Adv Neural Inf Process Syst 34:23309\u201323320","journal-title":"Adv Neural Inf Process Syst"},{"key":"2895_CR12","unstructured":"Zhang M, Sapra K, Fidler S, et\u00a0al (2020) Personalized federated learning with first order model optimization. arXiv:2012.08565"},{"key":"2895_CR13","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":"2895_CR14","doi-asserted-by":"crossref","unstructured":"Xie W, Feng Y, Gu S, et\u00a0al (2021) Importance-based neuron allocation for multilingual neural machine translation. arXiv:2107.06569","DOI":"10.18653\/v1\/2021.acl-long.445"},{"key":"2895_CR15","unstructured":"Tang Y, Tran C, Li X, et\u00a0al (2020) Multilingual translation with extensible multilingual pretraining and finetuning. arXiv:2008.00401"},{"issue":"3","key":"2895_CR16","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1038\/s42256-023-00626-4","volume":"5","author":"N Ding","year":"2023","unstructured":"Ding N, Qin Y, Yang G et al (2023) Parameter-efficient fine-tuning of large-scale pre-trained language models. Nat Mach Intell 5(3):220\u2013235","journal-title":"Nat Mach Intell"},{"key":"2895_CR17","unstructured":"Xu H, Kim YJ, Sharaf A, et\u00a0al (2023) A paradigm shift in machine translation: Boosting translation performance of large language models. arXiv:2309.11674"},{"key":"2895_CR18","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang L, Shi S et al (2020) Go from the general to the particular: Multi-domain translation with domain transformation networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 9233\u20139241","DOI":"10.1609\/aaai.v34i05.6461"},{"key":"2895_CR19","unstructured":"Roosta T, Passban P, Chadha A (2021) Communication-efficient federated learning for neural machine translation. arXiv:2112.06135"},{"key":"2895_CR20","doi-asserted-by":"crossref","unstructured":"Passban P, Roosta T, Gupta R, et\u00a0al (2022) Training mixed-domain translation models via federated learning. arXiv:2205.01557","DOI":"10.18653\/v1\/2022.naacl-main.186"},{"key":"2895_CR21","doi-asserted-by":"crossref","unstructured":"Liu Y, Bi X, Li L, et\u00a0al (2023) Communication efficient federated learning for multilingual neural machine translation with adapter. arXiv:2305.12449","DOI":"10.18653\/v1\/2023.findings-acl.327"},{"issue":"6","key":"2895_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3660522","volume":"42","author":"H Peng","year":"2024","unstructured":"Peng H, Zhang J, Huang X et al (2024) Unsupervised social bot detection via structural information theory. ACM Trans Inform Syst 42(6):1\u201342","journal-title":"ACM Trans Inform Syst"},{"key":"2895_CR23","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1145\/3589335.3651931","volume":"2024","author":"YW Chu","year":"2024","unstructured":"Chu YW, Han DJ, Brinton CG (2024) Only send what you need: learning to communicate efficiently in federated multilingual machine translation. Companion Proc ACM Web Confer 2024:1548\u20131557","journal-title":"Companion Proc ACM Web Confer"},{"key":"2895_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112501","volume":"304","author":"X Zhang","year":"2024","unstructured":"Zhang X, Wang J, Bao W et al (2024) Structural graph federated learning: exploiting high-dimensional information of statistical heterogeneity. Knowl-Based Syst 304:112501","journal-title":"Knowl-Based Syst"},{"key":"2895_CR25","first-page":"1314","volume":"2023","author":"Y Yang","year":"2023","unstructured":"Yang Y, Yang R, Peng H et al (2023) Fedack: Federated adversarial contrastive knowledge distillation for cross-lingual and cross-model social bot detection. Proceedings of the ACM Web Conference 2023:1314\u20131323","journal-title":"Proceedings of the ACM Web Conference"},{"key":"2895_CR26","doi-asserted-by":"crossref","unstructured":"Zhao H, Zhang W, Sun H, et\u00a0al (2024b) Personalized fuzzy federated prompt tuning for re-identification. In: 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI), IEEE, pp 110\u2013115","DOI":"10.1109\/DTPI61353.2024.10778696"},{"key":"2895_CR27","doi-asserted-by":"crossref","unstructured":"Zhang X, Wang J, Bao W, et\u00a0al (2024b) Improving generalization and personalization in model-heterogeneous federated learning. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2024.3405190"},{"key":"2895_CR28","doi-asserted-by":"crossref","unstructured":"Zhao H, Liu Q, Sun H, et\u00a0al (2024a) Community awareness personalized federated learning for defect detection. IEEE Trans Comput Soc Syst","DOI":"10.1109\/TCSS.2024.3405556"},{"key":"2895_CR29","first-page":"60397","volume":"36","author":"Z Yang","year":"2023","unstructured":"Yang Z, Zhang Y, Zheng Y et al (2023) Fedfed: feature distillation against data heterogeneity in federated learning. Adv Neural Inf Process Syst 36:60397\u201360428","journal-title":"Adv Neural Inf Process Syst"},{"key":"2895_CR30","unstructured":"Arivazhagan MG, Aggarwal V, Singh AK, et\u00a0al (2019) Federated learning with personalization layers. arXiv:1912.00818"},{"key":"2895_CR31","unstructured":"Deng Y, Kamani MM, Mahdavi M (2020) Adaptive personalized federated learning. arXiv:2003.13461"},{"key":"2895_CR32","unstructured":"Lyu L, Yu H, Ma X et al (2022) Privacy and robustness in federated learning: attacks and defenses. IEEE Trans Neural Netw Learn Syst"},{"key":"2895_CR33","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2020) Making monolingual sentence embeddings multilingual using knowledge distillation. arXiv:2004.09813","DOI":"10.18653\/v1\/2020.emnlp-main.365"},{"key":"2895_CR34","unstructured":"Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: Proceedings of machine translation summit x: papers, pp 79\u201386"},{"key":"2895_CR35","unstructured":"McMahan B, Moore E, Ramage D, et\u00a0al (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, PMLR, pp 1273\u20131282"},{"key":"2895_CR36","doi-asserted-by":"crossref","unstructured":"Weller O, Marone M, Braverman V, et\u00a0al (2022) Pretrained models for multilingual federated learning. arXiv:2206.02291","DOI":"10.18653\/v1\/2022.naacl-main.101"},{"key":"2895_CR37","doi-asserted-by":"crossref","unstructured":"Luo J, Wu S (2022) Adapt to adaptation: learning personalization for cross-silo federated learning. In: IJCAI: proceedings of the conference, NIH Public Access","DOI":"10.24963\/ijcai.2022\/301"},{"key":"2895_CR38","doi-asserted-by":"crossref","unstructured":"Post M (2018) A call for clarity in reporting bleu scores. arXiv:1804.08771","DOI":"10.18653\/v1\/W18-6319"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02895-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02895-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02895-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T11:51:59Z","timestamp":1772279519000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02895-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,28]]},"references-count":38,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["2895"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02895-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,28]]},"assertion":[{"value":"13 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2026","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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"152"}}