Study of the performance and scalability of federated learning for medical imaging with intermittent clients. (arXiv:2207.08581v3 [cs.LG] UPDATED)

Federated learning is a data decentralization privacy-preserving technique
used to perform machine or deep learning in a secure way. In this paper we
present theoretical aspects about federated learning, such as the presentation
of an aggregation operator, different types of federated learning, and issues
to be taken into account in relation to the distribution of data from the
clients, together with the exhaustive analysis of a use case where the number
of clients varies. Specifically, a use case of medical …

arxiv federated learning imaging intermittent medical medical imaging performance scalability study