Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges. (arXiv:2201.08135v1 [cs.CR])

Federated learning is a machine learning paradigm that emerges as a solution
to the privacy-preservation demands in artificial intelligence. As machine
learning, federated learning is threatened by adversarial attacks against the
integrity of the learning model and the privacy of data via a distributed
approach to tackle local and global learning. This weak point is exacerbated by
the inaccessibility of data in federated learning, which makes harder the
protection against adversarial attacks and evidences the need to furtherance
the research …

attacks challenges concepts survey threats