ABC-FL: Anomalous and Benign client Classification in Federated Learning. (arXiv:2108.04551v3 [cs.LG] UPDATED)

Federated Learning is a distributed machine learning framework designed for
data privacy preservation i.e., local data remain private throughout the entire
training and testing procedure. Federated Learning is gaining popularity
because it allows one to use machine learning techniques while preserving
privacy. However, it inherits the vulnerabilities and susceptibilities raised
in deep learning techniques. For instance, Federated Learning is particularly
vulnerable to data poisoning attacks that may deteriorate its performance and
integrity due to its distributed nature and inaccessibility to …

classification client federated learning