Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. (arXiv:2207.10804v1 [cs.CR])

Collaboration among multiple data-owning entities (e.g., hospitals) can
accelerate the training process and yield better machine learning models due to
the availability and diversity of data. However, privacy concerns make it
challenging to exchange data while preserving confidentiality. Federated
Learning (FL) is a promising solution that enables collaborative training
through exchange of model parameters instead of raw data. However, most
existing FL solutions work under the assumption that participating clients are
\emph{honest} and thus can fail against poisoning attacks from …

attacks federated learning medical medical imaging poisoning