Improving Performance of Private Federated Models in Medical Image Analysis. (arXiv:2304.05127v1 [cs.CR])

Federated learning (FL) is a distributed machine learning (ML) approach that
allows data to be trained without being centralized. This approach is
particularly beneficial for medical applications because it addresses some key
challenges associated with medical data, such as privacy, security, and data
ownership. On top of that, FL can improve the quality of ML models used in
medical applications. Medical data is often diverse and can vary significantly
depending on the patient population, making it challenging to develop ML …

addresses analysis applications challenges data data ownership distributed federated learning key key challenges machine machine learning making medical medical data ml models ownership performance privacy private quality security