164 citations · 339 across the 12 of their papers we have counts for
4 papers · 1 filter
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Rongmei Lin, Yonghui Xiao, Tien-Ju Yang +4
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been w…
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.…
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 (…
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…