  Federated Learning (FL) has been proposed as a privacy-preserving solution
for machine learning. However, recent works have reported that FL can leak
private client data through membership inference attacks. In this paper, we
show that the effectiveness of these attacks on the clients negatively
correlates with the size of the client's datasets and model complexity. Based
on this finding, we study the capabilities of model-agnostic Federated Learning
to preserve privacy, as it enables the use of models of varying complexity in
the clients. To systematically study this topic, we first propose a taxonomy of
model-agnostic FL methods according to the strategies adopted by the clients to
select the sub-models from the server's model. This taxonomy provides a
framework for existing model-agnostic FL approaches and leads to the proposal
of new FL methods to fill the gaps in the taxonomy. Next, we analyze the
privacy-performance trade-off of all the model-agnostic FL architectures as per
the proposed taxonomy when subjected to 3 different membership inference
attacks on the CIFAR-10 and CIFAR-100 vision datasets. In our experiments, we
find that randomness in the strategy used to select the server's sub-model to
train the clients' models can control the clients' privacy while keeping
competitive performance on the server's side.
