Addressing Membership Inference Attack in Federated Learning with Model
  Compression
Gergely Dániel Németh Miguel Ángel Lozano Novi Quadrianto Nuria Oliver
Federated Learning (FL) has been proposed as a privacy-preserving solution
for machine learning. However, recent works have shown that Federated Learning
can leak private client data through membership attacks. In this paper, we show
that the effectiveness of these attacks on the clients negatively correlates
with the size of the client datasets and model complexity. Based on this
finding, we propose model-agnostic Federated Learning as a privacy-enhancing
solution because it enables the use of models of varying complexity in the
clients. To this end, we present $\texttt{MaPP-FL}$, a novel privacy-aware FL
approach that leverages model compression on the clients while keeping a full
model on the server. We compare the performance of $\texttt{MaPP-FL}$ against
state-of-the-art model-agnostic FL methods on the CIFAR-10, CIFAR-100, and
FEMNIST vision datasets. Our experiments show the effectiveness of
$\texttt{MaPP-FL}$ in preserving the clients' and the server's privacy while
achieving competitive classification accuracies.