19 citations · 47 across the 5 of their papers we have counts for
8 papers
Revealing and Protecting Labels in Distributed Training
Trung Dang, Om Thakkar, Swaroop Ramaswamy +3
Distributed learning paradigms such as federated learning often involve transmission of model updates, or gradients, over a network, thereby avoiding transmission of private data.…
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It
Trung Dang, Om Thakkar, Swaroop Ramaswamy +3
End-to-end Automatic Speech Recognition (ASR) models are commonly trained over spoken utterances using optimization methods like Stochastic Gradient Descent (SGD). In distributed s…
Training Production Language Models without Memorizing User Data
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews +3
This paper presents the first consumer-scale next-word prediction (NWP) model trained with Federated Learning (FL) while leveraging the Differentially Private Federated Averaging (…
Privacy Amplification via Random Check-Ins
Borja Balle, Peter Kairouz, H. Brendan McMahan +2
Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fundamental building block in many applications for learning over sensitive data. Two standard approaches, priva…
Understanding Unintended Memorization in Federated Learning
Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews +1
Recent works have shown that generative sequence models (e.g., language models) have a tendency to memorize rare or unique sequences in the training data. Since useful models are o…
Evading Curse of Dimensionality in Unconstrained Private GLMs via Private Gradient Descent
Shuang Song, Thomas Steinke, Om Thakkar +1
We revisit the well-studied problem of differentially private empirical risk minimization (ERM). We show that for unconstrained convex generalized linear models (GLMs), one can obt…