  Federated Learning (FL) allows multiple participants to train machine
learning models collaboratively by keeping their datasets local while only
exchanging model updates. Alas, this is not necessarily free from privacy and
robustness vulnerabilities, e.g., via membership, property, and backdoor
attacks. This paper investigates whether and to what extent one can use
differential Privacy (DP) to protect both privacy and robustness in FL. To this
end, we present a first-of-its-kind evaluation of Local and Central
Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility
and effectiveness. Our experiments show that both DP variants do d fend against
backdoor attacks, albeit with varying levels of protection-utility trade-offs,
but anyway more effectively than other robustness defenses. DP also mitigates
white-box membership inference attacks in FL, and our work is the first to show
it empirically. Neither LDP nor CDP, however, defend against property
inference. Overall, our work provides a comprehensive, re-usable measurement
methodology to quantify the trade-offs between robustness/privacy and utility
in differentially private FL.
