Fig. 1. Federated Learning Platform.

Participating sites (a) and FL procedure (b). a North America: Stanford Children’s Hospital (ST—Palo Alto, California), Seattle Children’s Hospital (SE—Seattle, Washington), Phoenix Children’s Hospital (PH—Phoenix, Arizona), Primary Children’s Hospital (UT—Salt Lake City, Utah), Children’s Hospital Orange County (CH—Orange County, California), Dayton Children’s Hospital (DY—Dayton, Ohio), Indiana University Riley Children’s (IN—Indianapolis, Indiana), Lurie Children’s Hospital of Chicago (CG—Chicago, Illinois), NYU Langone Medical Center (NY—New York City, New York), Children’s Hospital of Philadelphia (CP—Philadelphia, Pennsylvania), Duke Children’s Hospital (DU—Durham, North Carolina), Boston Children’s Hospital (BO—Boston, Massachusetts), Toronto Sick Kids Hospital (TO—Toronto, Canada); Europe: Great Ormand Street Hospital (GO—London, United Kingdom),Tepecik Health Sciences (TK—Izmir, Turkey), Koç University (KC—Istanbul, Turkey); North Africa: Centre International Carthage Médical (TU—Monastir, Tunisia); West Asia: Tehran University of Medical Sciences (TM—Tehran, Iran); Australia: The Children’s Hospital at Westmead (AU—Sydney, Australia). b Our FL framework incorporates FL warm-up on the largest sites and proximal regularization to learn on heterogeneous sites, but we report the best results with μ = 0.