Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation. (arXiv:2205.00107v2 [cs.LG] UPDATED)

This paper aims at jointly addressing two seemly conflicting issues in
federated learning: differential privacy (DP) and Byzantine-robustness, which
are particularly challenging when the distributed data are non-i.i.d.
(independent and identically distributed). The standard DP mechanisms add noise
to the transmitted messages, and entangles with robust stochastic gradient
aggregation to defend against Byzantine attacks. In this paper, we decouple the
two issues via robust stochastic model aggregation, in the sense that our
proposed DP mechanisms and the defense against Byzantine …

differential privacy lg privacy robustness