AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT. (arXiv:2303.13567v2 [cs.LG] UPDATED)

While it is well known that population differences from genetics, sex, race,
and environmental factors contribute to disease, AI studies in medicine have
largely focused on locoregional patient cohorts with less diverse data sources.
Such limitation stems from barriers to large-scale data share and ethical
concerns over data privacy. Federated learning (FL) is one potential pathway
for AI development that enables learning across hospitals without data share.
In this study, we show the results of various FL strategies on one …

ai development ai models arxiv data data privacy data sources development disease diverse environmental federated learning genetics hospitals medicine patient population privacy race scale sex strategies studies study