1.1k citations · 1.6k across the 34 of their papers we have counts for
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Generative Models for Effective ML on Private, Decentralized Datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage +5
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of ra…
Federated Evaluation of On-device Personalization
Kangkang Wang, Rajiv Mathews, Chloé Kiddon +3
Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers. In this work, we describe…
Federated Learning of N-gram Language Models
Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews +4
We propose algorithms to train production-quality n-gram language models using federated learning. Federated learning is a distributed computation platform that can be used to trai…
Federated Learning for Emoji Prediction in a Mobile Keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao +1
We show that a word-level recurrent neural network can predict emoji from text typed on a mobile keyboard. We demonstrate the usefulness of transfer learning for predicting emoji b…
Federated Learning Of Out-Of-Vocabulary Words
Mingqing Chen, Rajiv Mathews, Tom Ouyang +1
We demonstrate that a character-level recurrent neural network is able to learn out-of-vocabulary (OOV) words under federated learning settings, for the purpose of expanding the vo…