Federated Learning with Differential Privacy
Adrien Banse Jan Kreischer Xavier Oliva i Jürgens
Federated learning (FL), as a type of distributed machine learning, is
capable of significantly preserving client's private data from being shared
among different parties. Nevertheless, private information can still be
divulged by analyzing uploaded parameter weights from clients. In this report,
we showcase our empirical benchmark of the effect of the number of clients and
the addition of differential privacy (DP) mechanisms on the performance of the
model on different types of data. Our results show that non-i.i.d and small
datasets have the highest decrease in performance in a distributed and
differentially private setting.