{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T10:12:54Z","timestamp":1769508774335,"version":"3.49.0"},"reference-count":0,"publisher":"Il Pensiero Scientifico Editore","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,1]]},"abstract":"<jats:p>AI is transforming neurology, providing powerful diagnostic and therapeutic tools, yet handling sensitive clinical data involves substantial privacy risks. Federated Learning addresses these issues by training models locally within hospitals and sharing only their weights or gradients for final training, achieving similar or superior performance compared to centralized models in Stroke, Alzheimer\u2019s, and Parkinson\u2019s disease.<\/jats:p>","DOI":"10.1701\/4573.45790","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T22:45:12Z","timestamp":1769467512000},"source":"Crossref","is-referenced-by-count":0,"title":["Federated Learning in neurologia: applicazioni nello stroke, malattia di Alzheimer e di Parkinson"],"prefix":"10.1701","volume":"116","author":[{"given":"Cosimo","family":"Guerra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosario","family":"De Feo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55112","container-title":["Recenti Progressi in Medicina"],"original-title":[],"deposited":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T22:45:12Z","timestamp":1769467512000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.recentiprogressi.it\/articoli.php?archivio=yes&vol_id=4573&id=45790"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"references-count":0,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10,1]]}},"URL":"https:\/\/doi.org\/10.1701\/4573.45790","relation":{},"ISSN":["0034-1193"],"issn-type":[{"value":"0034-1193","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,1]]}}}