  Federated Learning (FL) is emerging as a promising paradigm of
privacy-preserving machine learning, which trains an algorithm across multiple
clients without exchanging their data samples. Recent works highlighted several
privacy and robustness weaknesses in FL and addressed these concerns using
local differential privacy (LDP) and some well-studied methods used in
conventional ML, separately. However, it is still not clear how LDP affects
adversarial robustness in FL. To fill this gap, this work attempts to develop a
comprehensive understanding of the effects of LDP on adversarial robustness in
FL. Clarifying the interplay is significant since this is the first step
towards a principled design of private and robust FL systems. We certify that
local differential privacy has both positive and negative effects on
adversarial robustness using theoretical analysis and empirical verification.
