Fairness and Privacy in Federated Learning and Their Implications in Healthcare. (arXiv:2308.07805v1 [cs.CR])

Currently, many contexts exist where distributed learning is difficult or
otherwise constrained by security and communication limitations. One common
domain where this is a consideration is in Healthcare where data is often
governed by data-use-ordinances like HIPAA. On the other hand, larger sample
sizes and shared data models are necessary to allow models to better generalize
on account of the potential for more variability and balancing underrepresented
classes. Federated learning is a type of distributed learning model that allows
data …

communication data distributed domain fairness federated learning healthcare hipaa privacy sample security