{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T05:22:54Z","timestamp":1787376174696,"version":"build-2736575974"},"reference-count":15,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015550","name":"Ministry of Health","doi-asserted-by":"publisher","award":["FSC 2014\u20132020"],"award-info":[{"award-number":["FSC 2014\u20132020"]}],"id":[{"id":"10.13039\/501100015550","id-type":"DOI","asserted-by":"publisher"}]},{"award":["FSC 2014\u20132020"],"award-info":[{"award-number":["FSC 2014\u20132020"]}],"id":[{"id":"https:\/\/ror.org\/00789fa95","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007706","name":"Ministero delle Imprese e del Made in Italy","doi-asserted-by":"publisher","award":["CUP: D33B22000060001"],"award-info":[{"award-number":["CUP: D33B22000060001"]}],"id":[{"id":"10.13039\/501100007706","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007707","name":"Istituto Nazionale per l'Assicurazione Contro gli Infortuni sul Lavoro","doi-asserted-by":"publisher","award":["no grant number"],"award-info":[{"award-number":["no grant number"]}],"id":[{"id":"10.13039\/501100007707","id-type":"DOI","asserted-by":"publisher"}]},{"award":["no grant number"],"award-info":[{"award-number":["no grant number"]}],"id":[{"id":"https:\/\/ror.org\/01t264m74","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Eye diseases, particularly glaucoma and cataracts, are the leading causes of visual impairment, compromising quality of life. Automatic classification of these diseases can lead to more accurate and timely diagnosis, thereby limiting their progression and complications. In recent years, advances in Deep Learning (DL) have shown promising results in the study of images in ophthalmology. However, traditional DL models are based on a centralised approach to data, sharing sensitive patient information and compromising privacy; moreover, the models are often difficult to interpret. In this paper we propose a method aimed to solve the issues of privacy and transparency in decision-making related to eye diseases detection and localisation. As a matter of fact, we consider Federated Learning (FL), an approach based on data decentralisation that enables collaborative learning between different clients and sends only the model weights to the central server. In this way, sensitive patient data are not shared, ensuring security and privacy. With regard to eye disease classification we exploit a Vision Transformer, which allows global relationships within retinal images to be highlighted, improving representation capabilities compared to traditional convolutional architectures. Furthermore, the proposed method also aims to make the model explainable using explainability techniques, in this way we make diagnostic decisions transparent. The experimental analysis shows an accuracy of 0.8480, a precision of 0.8645, a recall of 0.8477, showing the effectiveness of the proposed method on eye disease detection.<\/jats:p>","DOI":"10.3390\/s26165288","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:05:19Z","timestamp":1787241919000},"page":"5288","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Federated Machine Learning Approach for the Detection and Visualisation of Eye Diseases Using Activation Maps"],"prefix":"10.3390","volume":"26","author":[{"given":"Filomena","family":"Niro","sequence":"first","affiliation":[{"name":"Azienda Sanitaria Regionale del Molise\u2014ASReM, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miriam Di","family":"Renzo","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0130-8727","authenticated-orcid":false,"given":"Patrizia","family":"Agnello","sequence":"additional","affiliation":[{"name":"Istituto Nazionale per l\u2019Assicurazione Contro gli Infortuni sul Lavoro, 00144 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Petyx","sequence":"additional","affiliation":[{"name":"Istituto Nazionale per l\u2019Assicurazione Contro gli Infortuni sul Lavoro, 00144 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Martinelli","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking, National Research Council of Italy (CNR), 87036 Rende, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurizio","family":"Maddalena","sequence":"additional","affiliation":[{"name":"IRCCS Istituto Nazionale Tumori, Fondazione Pascale, 80131 Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Cesarelli","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Santone","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-1657","authenticated-orcid":false,"given":"Francesco","family":"Mercaldo","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101030","DOI":"10.1016\/j.xops.2025.101030","article-title":"Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography","volume":"6","author":"Nabil","year":"2026","journal-title":"Ophthalmol. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1109\/TPDS.2023.3264473","article-title":"DRFL: Federated Learning in Diabetic Retinopathy Grading Using Fundus Images","volume":"34","author":"Mohan","year":"2023","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1136\/bjo-2023-324188","article-title":"Developing a privacy-preserving deep learning model for glaucoma detection: A multicentre study with federated learning","volume":"108","author":"Ran","year":"2024","journal-title":"Br. J. Ophthalmol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Baptista, T., Soares, C., Oliveira, T., and Soares, F. (2023). Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images. Appl. Sci., 13.","DOI":"10.3390\/app132111620"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","article-title":"The future of digital health with federated learning","volume":"3","author":"Rieke","year":"2020","journal-title":"npj Digit. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14997","DOI":"10.1038\/ncomms14997","article-title":"Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics","volume":"8","author":"Kim","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s10544-025-00740-7","article-title":"Enhancing glaucoma care with smart contact lenses: An overview of recent developments","volume":"27","author":"Fardoost","year":"2025","journal-title":"Biomed. Microdevices"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"89","DOI":"10.14257\/ijgdc.2018.11.1.09","article-title":"Classification of diabetic retinopathy images by using deep learning models","volume":"11","author":"Dutta","year":"2018","journal-title":"Int. J. Grid Distrib. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kwasigroch, A., Jarzembinski, B., and Grochowski, M. (2018). Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), IEEE.","DOI":"10.1109\/IIPHDW.2018.8388337"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, G., Gallardo, J., Mauricio, A., L\u00f3pez, J., and Del Carpio, C. (2017). Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. Proceedings of the International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-319-68612-7_72"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","article-title":"Convolutional neural networks for diabetic retinopathy","volume":"90","author":"Pratt","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s10916-007-9113-9","article-title":"Automated identification of diabetic retinopathy stages using digital fundus images","volume":"32","author":"Nayak","year":"2008","journal-title":"J. Med. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5288\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T04:24:55Z","timestamp":1787372695000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,20]]},"references-count":15,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165288"],"URL":"https:\/\/doi.org\/10.3390\/s26165288","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,20]]}}}