Privacy Amplification via Random Participation in Federated Learning. (arXiv:2205.01556v1 [cs.LG])

Running a randomized algorithm on a subsampled dataset instead of the entire
dataset amplifies differential privacy guarantees. In this work, in a federated
setting, we consider random participation of the clients in addition to
subsampling their local datasets. Since such random participation of the
clients creates correlation among the samples of the same client in their
subsampling, we analyze the corresponding privacy amplification via non-uniform
subsampling. We show that when the size of the local datasets is small, the
privacy …

amplification lg participation privacy