Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
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We study the problem of approximating stationary points of Lipschitz and smooth functions under (ε,δ)-differential privacy (DP) in both the finite-sum and stochastic settings. A point w is called an α-stationary point of a function F:ℝ^d→ℝ if ∇ F(w)≤α. We provide a new efficient algorithm that finds an Õ([√(d)/nε]^2/3)-stationary point in the finite-sum setting, where n is the number of samples. This improves on the previous best rate of Õ([√(d)/nε]^1/2). We also give a new construction that improves over the existing rates in the stochastic optimization setting, where the goal is to find approximate stationary points of the population risk. Our construction finds a Õ(1/n^1/3 + [√(d)/nε]^1/2)-stationary point of the population risk in time linear in n. Furthermore, under the additional assumption of convexity, we completely characterize the sample complexity of finding stationary points of the population risk (up to polylog factors) and show that the optimal rate on population stationarity is Θ̃(1/√(n)+√(d)/nε). Finally, we show that our methods can be used to provide dimension-independent rates of O(1/√(n)+min([√(rank)/nε]^2/3,1/(nε)^2/5)) on population stationarity for Generalized Linear Models (GLM), where rank is the rank of the design matrix, which improves upon the previous best known rate.
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