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stat.ML2019
Computing Tight Differential Privacy Guarantees Using FFT
Antti Koskela, Joonas Jälkö, Antti Honkela
Differentially private (DP) machine learning has recently become popular. The privacy loss of DP algorithms is commonly reported using -DP. In this paper, we propo…
stat.ML2018
Learning Rate Adaptation for Federated and Differentially Private Learning
Antti Koskela, Antti Honkela
We propose an algorithm for the adaptation of the learning rate for stochastic gradient descent (SGD) that avoids the need for validation set use. The idea for the adaptiveness com…