{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:25:38Z","timestamp":1792787138884,"version":"4.5.0"},"reference-count":0,"publisher":"SPIE-Intl Soc Optical Eng","issue":"01","funder":[{"name":"Lower Saxony Ministry of Science and Culture"},{"name":"the Endowed Chair of Applied AI at the University of Oldenburg","id":[{"id":"https:\/\/ror.org\/033n9gh91","id-type":"ROR","asserted-by":"crossref"}]},{"name":"German Federal Ministry of Education and Research","award":["01IW23002"],"award-info":[{"award-number":["01IW23002"]}],"id":[{"id":"https:\/\/ror.org\/04pz7b180","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2026,1,5]]},"abstract":"<jats:p>PurposeCentralized machine learning often struggles with limited data access and expert involvement. We investigate decentralized approaches that preserve data privacy while enabling collaborative model training for medical imaging tasks.ApproachWe explore asynchronous federated learning (FL) using the FL with buffered asynchronous aggregation (FedBuff) algorithm for classifying optical coherence tomography (OCT) retina images. Unlike synchronous algorithms such as FedAvg, which require all clients to participate simultaneously, FedBuff supports independent client updates. We compare its performance to both centralized models and FedAvg. In addition, we develop a browser-based proof-of-concept system using modern web technologies to assess the feasibility and limitations of interactive, collaborative learning in real-world settings.ResultsFedBuff performs well in binary OCT classification tasks but shows reduced accuracy in more complex, multiclass scenarios. FedAvg achieves results comparable to centralized training, consistent with previous findings. Although FedBuff underperforms compared with FedAvg and centralized models, it still delivers acceptable accuracy in less complex settings. The browser-based prototype demonstrates the potential for accessible, user-driven FL systems but also highlights technical limitations in current web standards, especially regarding local computation and communication efficiency.ConclusionAsynchronous FL via FedBuff offers a promising, privacy-preserving approach for medical image classification, particularly when synchronous participation is impractical. However, its scalability to complex classification tasks remains limited. Web-based implementations have the potential to broaden access to collaborative AI tools, but limitations of the current technologies need to be further investigated.<\/jats:p>","DOI":"10.1117\/1.jmi.13.1.014501","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T15:09:42Z","timestamp":1767625782000},"page":"1-22","source":"Crossref","is-referenced-by-count":2,"title":["Asynchronous federated learning for web-based OCT image analysis"],"prefix":"10.1117","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1479-7690","authenticated-orcid":false,"given":"Hasan Md Tusfiqur","family":"Alam","sequence":"first","affiliation":[{"name":"Deutsches Forschungszentrum f\u00fcr K\u00fcnstliche Intelligenz GmbH"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Maurer","sequence":"additional","affiliation":[{"name":"Univ. des Saarlandes"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4984-6686","authenticated-orcid":false,"given":"Abdulrahman","family":"Mohamed Selim","sequence":"additional","affiliation":[{"name":"Deutsches Forschungszentrum f\u00fcr K\u00fcnstliche Intelligenz GmbH"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Eiletz","sequence":"additional","affiliation":[{"name":"ProOmea GmbH"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6730-2466","authenticated-orcid":false,"given":"Michael","family":"Barz","sequence":"additional","affiliation":[{"name":"Deutsches Forschungszentrum f\u00fcr K\u00fcnstliche Intelligenz GmbH"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Sonntag","sequence":"additional","affiliation":[{"name":"Deutsches Forschungszentrum f\u00fcr K\u00fcnstliche Intelligenz GmbH"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","container-title":["Journal of Medical Imaging"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-13\/issue-01\/014501\/Asynchronous-federated-learning-for-web-based-OCT-image-analysis\/10.1117\/1.JMI.13.1.014501.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:24:51Z","timestamp":1790195091000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-13\/issue-01\/014501\/Asynchronous-federated-learning-for-web-based-OCT-image-analysis\/10.1117\/1.JMI.13.1.014501.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2026,1,1]]}},"URL":"https:\/\/doi.org\/10.1117\/1.jmi.13.1.014501","relation":{},"ISSN":["2329-4302","2329-4310"],"issn-type":[{"value":"2329-4302","type":"print"},{"value":"2329-4310","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]}}}