2 citations · 8 across the 12 of their papers we have counts for
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cs.LG2021★ 2 cited
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
Andrew Lowy, Meisam Razaviyayn
This paper studies federated learning (FL)--especially cross-silo FL--with data from people who do not trust the server or other silos. In this setting, each silo (e.g. hospital) h…
cs.LG2021★ 2 cited
A Stochastic Optimization Framework for Fair Risk Minimization
Andrew Lowy, Sina Baharlouei, Rakesh Pavan +2
Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibil…
cs.LG2021★ 2 cited
Output Perturbation for Differentially Private Convex Optimization: Faster and More General
Andrew Lowy, Meisam Razaviyayn
Finding efficient, easily implementable differentially private (DP) algorithms that offer strong excess risk bounds is an important problem in modern machine learning. To date, mos…