{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T22:41:45Z","timestamp":1769294505094,"version":"3.49.0"},"reference-count":11,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"ABSTRACT<\/jats:title>\n This paper proposes a 6G\u2010driven federated edge intelligence framework for multi\u2010center stroke lesion segmentation. Lightweight MobileStroke\u2010U\u2010Net is deployed at the edge of each participating hospital for distributed MRI segmentation, enabling federated learning without data sharing. Gradient adaptive weighted aggregation (GradAdapt) is used at the Center Server layer to alleviate heterogeneous distribution offset across multiple centers. The global model is trained in the cloud and combined with homomorphic encryption and blockchain auditing to achieve end\u2010to\u2010end privacy protection. Experiments are conducted on two major multi\u2010center datasets, ATLASv2.0 and ISLES22: Compared with the best single model, FL\u2010MobileStroke\u2010U\u2010Net improves Dice by 1%\u20132% on both datasets. FL\u2010MobileStroke\u2010U\u2010Net improves Dice from 0.6458 to 0.6611 on ATLAS v2.0, and the 95% Hausdorff distance changes from 22.9573 to 21.2032; on ISLES22, Dice increases from 0.7527 to 0.7632, and the 95% Hausdorff distance changes from 11.8847 to 11.0762. The results show that the proposed multi\u2010center federated framework effectively balances privacy, security, and efficiency, significantly improves cross\u2010center segmentation performance, and provides a new approach for 6G\u2010enabled intelligent stroke management.<\/jats:p>","DOI":"10.1002\/itl2.70184","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T13:25:37Z","timestamp":1763472337000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["6G<\/scp>\n \u2010Enabled Federated Edge Intelligence: Multi\u2010Center Stroke Lesion Segmentation"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2037-179X","authenticated-orcid":false,"given":"Siyu","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Applied Technology Anshan Normal University Liaoning China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.20.7.864"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0186-1"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3251404"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.1900630"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3351600"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01401-7"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01875-5"},{"key":"e_1_2_9_9_1","first-page":"234","volume-title":"U\u2010Net: Convolutional Networks for Biomedical Image Segmentation","author":"Ronneberger O.","year":"2015"},{"key":"e_1_2_9_10_1","unstructured":"J.Chen Y.Lu Q.Yu et al. \u201cTransunet: Transformers Make Strong Encoders for Medical Image Segmentation.arXiv Preprint arXiv:2102.04306. 2021\u201d."},{"key":"e_1_2_9_11_1","doi-asserted-by":"crossref","unstructured":"A.Hatamizadeh V.Nath Y.Tang et al. \u201cSwin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. 2021; 272\u2013284\u201d.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70184","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/itl2.70184","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70184","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T03:38:11Z","timestamp":1769139491000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":11,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1002\/itl2.70184"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70184","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"value":"2476-1508","type":"print"},{"value":"2476-1508","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]},"assertion":[{"value":"2025-08-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70184"}}