{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:06:53Z","timestamp":1774645613760,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819672370","type":"print"},{"value":"9789819672387","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-96-7238-7_14","type":"book-chapter","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T14:25:40Z","timestamp":1753194340000},"page":"169-181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Federated Learning with\u00a0SOA: An Approach to\u00a0Tackle Non-IID Data Challenges"],"prefix":"10.1007","author":[{"given":"Loukas","family":"Papadopoulos","sequence":"first","affiliation":[]},{"given":"Nemania","family":"Borovits","sequence":"additional","affiliation":[]},{"given":"George","family":"Manias","sequence":"additional","affiliation":[]},{"given":"Damian Andrew","family":"Tamburri","sequence":"additional","affiliation":[]},{"given":"Willem-Jan","family":"Van Den Heuvel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Antunes, R.S., Andr\u00e9\u00a0da Costa, C., K\u00fcderle, A., Yari, I.A., Eskofier, B.: Federated learning for healthcare: systematic review and architecture proposal. ACM Trans. Intell. Syst. Technol. (TIST) 13(4), 1\u201323 (2022)","DOI":"10.1145\/3501813"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Borovits, N., Bardelloni, G., Tamburri, D.A., Van Den\u00a0Heuvel, W.J.: Anonymization-as-a-service: the service center transcripts industrial case. In: International Conference on Service-Oriented Computing, pp. 261\u2013275. Springer (2023)","DOI":"10.1007\/978-3-031-48424-7_19"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Briggs, C., Fan, Z., Andras, P.: Federated learning with hierarchical clustering of local updates to improve training on non-IID data. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20139. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"issue":"2","key":"14_CR4","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s10579-013-9255-y","volume":"48","author":"B Desmet","year":"2014","unstructured":"Desmet, B., Hoste, V.: Fine-grained Dutch named entity recognition. Lang. Resour. Eval. 48(2), 307\u2013343 (2014)","journal-title":"Lang. Resour. Eval."},{"issue":"4","key":"14_CR5","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1162\/089120105775299177","volume":"31","author":"J Gao","year":"2005","unstructured":"Gao, J., Li, M., Huang, C.N., Wu, A.: Chinese word segmentation and named entity recognition: a pragmatic approach. Comput. Linguist. 31(4), 531\u2013574 (2005)","journal-title":"Comput. Linguist."},{"key":"14_CR6","unstructured":"Ge, S., Wu, F., Wu, C., Qi, T., Huang, Y., Xie, X.: FedNER: privacy-preserving medical named entity recognition with federated learning. arXiv preprint arXiv:2003.09288 (2020)"},{"issue":"5","key":"14_CR7","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.jbi.2012.04.008","volume":"45","author":"H Gurulingappa","year":"2012","unstructured":"Gurulingappa, H., Rajput, A.M., Roberts, A., Fluck, J., Hofmann-Apitius, M., Toldo, L.: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. J. Biomed. Inform. 45(5), 885\u2013892 (2012)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"14_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JIOT.2021.3095077","volume":"9","author":"A Imteaj","year":"2021","unstructured":"Imteaj, A., Thakker, U., Wang, S., Li, J., Amini, M.H.: A survey on federated learning for resource-constrained IoT devices. IEEE Internet Things J. 9(1), 1\u201324 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"14_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.18178\/ijmlc.2018.8.1.665","volume":"8","author":"N Kaoungku","year":"2018","unstructured":"Kaoungku, N., Suksut, K., Chanklan, R., Kerdprasop, K., Kerdprasop, N.: The silhouette width criterion for clustering and association mining to select image features. Int. J. Mach. Learn. Comput. 8(1), 69\u201373 (2018)","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"14_CR10","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.jbi.2015.03.010","volume":"55","author":"S Karimi","year":"2015","unstructured":"Karimi, S., Metke-Jimenez, A., Kemp, M., Wang, C.: Cadec: a corpus of adverse drug event annotations. J. Biomed. Inform. 55, 73\u201381 (2015)","journal-title":"J. Biomed. Inform."},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)","DOI":"10.18653\/v1\/N16-1030"},{"issue":"3","key":"14_CR12","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1109\/TNSE.2022.3150182","volume":"9","author":"Q Liu","year":"2022","unstructured":"Liu, Q., et al.: Asynchronous decentralized federated learning for collaborative fault diagnosis of PV stations. IEEE Trans. Netw. Sci. Eng. 9(3), 1680\u20131696 (2022)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Lo, S.K., Lu, Q., Paik, H.Y., Zhu, L.: FLRA: a reference architecture for federated learning systems. In: European Conference on Software Architecture, pp. 83\u201398. Springer (2021)","DOI":"10.1007\/978-3-030-86044-8_6"},{"key":"14_CR14","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Aguera\u00a0y Arcas, B.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"14_CR15","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol.\u00a026 (2013)"},{"issue":"7","key":"14_CR16","doi-asserted-by":"publisher","first-page":"5986","DOI":"10.1109\/JIOT.2019.2956615","volume":"7","author":"J Mills","year":"2019","unstructured":"Mills, J., Hu, J., Min, G.: Communication-efficient federated learning for wireless edge intelligence in IoT. IEEE Internet Things J. 7(7), 5986\u20135994 (2019)","journal-title":"IEEE Internet Things J."},{"key":"14_CR17","doi-asserted-by":"publisher","first-page":"101491","DOI":"10.1016\/j.is.2020.101491","volume":"91","author":"N Niknejad","year":"2020","unstructured":"Niknejad, N., Ismail, W., Ghani, I., Nazari, B., Bahari, M., et al.: Understanding service-oriented architecture (SOA): a systematic literature review and directions for further investigation. Inf. Syst. 91, 101491 (2020)","journal-title":"Inf. Syst."},{"key":"14_CR18","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s00778-007-0044-3","volume":"16","author":"MP Papazoglou","year":"2007","unstructured":"Papazoglou, M.P., Van Den Heuvel, W.J.: Service oriented architectures: approaches, technologies and research issues. VLDB J. 16, 389\u2013415 (2007)","journal-title":"VLDB J."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009), pp. 147\u2013155 (2009)","DOI":"10.3115\/1596374.1596399"},{"key":"14_CR21","unstructured":"Sang, E.F., De\u00a0Meulder, F.: Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. arXiv preprint arxiv:cs\/0306050 (2003)"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Shaalan, K., Raza, H.: Arabic named entity recognition from diverse text types. In: Proceedings of the 6th International Conference on Advances in Natural Language Processing, GoTAL 2008, Gothenburg, Sweden, 25\u201327 August 2008, pp. 440\u2013451. Springer (2008)","DOI":"10.1007\/978-3-540-85287-2_42"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Song, B., Li, F., Liu, Y., Zeng, X.: Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison. Brief. Bioinform. 22(6), bbab282 (2021)","DOI":"10.1093\/bib\/bbab282"},{"issue":"1","key":"14_CR24","first-page":"53","volume":"15","author":"A Thukral","year":"2023","unstructured":"Thukral, A., Dhiman, S., Meher, R., Bedi, P.: Knowledge graph enrichment from clinical narratives using NLP, NER, and biomedical ontologies for healthcare applications. Int. J. Inf. Technol. 15(1), 53\u201365 (2023)","journal-title":"Int. J. Inf. Technol."},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Veena, P., Kumar, M.A., Soman, K.: An effective way of word-level language identification for code-mixed Facebook comments using word-embedding via character-embedding. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1552\u20131556. IEEE (2017)","DOI":"10.1109\/ICACCI.2017.8126062"},{"key":"14_CR26","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","volume":"465","author":"H Zhu","year":"2021","unstructured":"Zhu, H., Xu, J., Liu, S., Jin, Y.: Federated learning on non-IID data: a survey. Neurocomputing 465, 371\u2013390 (2021)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Service-Oriented Computing \u2013 ICSOC 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-7238-7_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T20:34:24Z","timestamp":1774643664000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-7238-7_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,23]]},"ISBN":["9789819672370","9789819672387"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-7238-7_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,23]]},"assertion":[{"value":"23 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSOC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Service-Oriented Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tunis","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tunisia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsoc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icsoc2024.redcad.tn\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}