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Found 15 papers, 1 papers with code
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning
Almost none of the 2, 000+ languages spoken in Africa have widely available automatic speech recognition systems, and the required data is also only available for a few languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
UserLibri: A Dataset for ASR Personalization Using Only Text
We experiment on a user-clustered LibriSpeech corpus, supplemented with personalized text-only data for each user from Project Gutenberg.
Online Model Compression for Federated Learning with Large Models
This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost.
Scaling Language Model Size in Cross-Device Federated Learning
Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks.
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model
Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text.
Efficient and Private Federated Learning with Partially Trainable Networks
Through extensive experiments, we empirically show that Federated learning of Partially Trainable neural networks (FedPT) can result in superior communication-accuracy trade-offs, with up to $46\times$ reduction in communication cost, at a small accuracy cost.
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
Federated learning has been deployed to train machine learning models from decentralized client data on mobile devices in practice.
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network
Truecasing is the task of restoring the correct case (uppercase or lowercase) of noisy text generated either by an automatic system for speech recognition or machine translation or by humans.
Communication-Efficient Agnostic Federated Averaging
We propose a communication-efficient distributed algorithm called Agnostic Federated Averaging (or AgnosticFedAvg) to minimize the domain-agnostic objective proposed in Mohri et al. (2019), which is amenable to other private mechanisms such as secure aggregation.
Generative Models for Effective ML on Private, Decentralized Datasets
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact.
Federated Learning of N-gram Language Models
The n-gram language models trained with federated learning are compared to n-grams trained with traditional server-based algorithms using A/B tests on tens of millions of users of virtual keyboard.
Federated Learning Of Out-Of-Vocabulary Words
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 vocabulary of a virtual keyboard for smartphones without exporting sensitive text to servers.
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.
