Differentially Private SGDA for Minimax Problems. (arXiv:2201.09046v3 [cs.LG] UPDATED)

Stochastic gradient descent ascent (SGDA) and its variants have been the
workhorse for solving minimax problems. However, in contrast to the
well-studied stochastic gradient descent (SGD) with differential privacy (DP)
constraints, there is little work on understanding the generalization (utility)
of SGDA with DP constraints. In this paper, we use the algorithmic stability
approach to establish the generalization (utility) of DP-SGDA in different
settings. In particular, for the convex-concave setting, we prove that the
DP-SGDA can achieve an optimal utility …

lg problems