167 citations · 183 across the 2 of their papers we have counts for
4 papers
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
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 (…
Applied Federated Learning: Improving Google Keyboard Query Suggestions
Timothy Yang, Galen Andrew, Hubert Eichner +5
Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commer…
A General Approach to Adding Differential Privacy to Iterative Training Procedures
H. Brendan McMahan, Galen Andrew, Ulfar Erlingsson +4
In this work we address the practical challenges of training machine learning models on privacy-sensitive datasets by introducing a modular approach that minimizes changes to train…