Search Results for author:
Found 10 papers, 2 papers with code
Improving on-device speaker verification using federated learning with privacy
From these features, the model predicts speaker characteristic labels considered useful as side information.
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
We describe the design of our federated task processing system.
Enforcing fairness in private federated learning via the modified method of differential multipliers
This paper introduces an algorithm to enforce group fairness in private federated learning, where users' data does not leave their devices.
Training a Tokenizer for Free with Private Federated Learning
During private federated learning of the language model, we sample from the model, train a new tokenizer on the sampled sequences, and update the model embeddings.
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP.
SummaryMixing: A Linear-Complexity Alternative to Self-Attention for Speech Recognition and Understanding
Unfortunately, token mixing with self-attention takes quadratic time in the length of the speech utterance, slowing down inference as well as training and increasing memory consumption.
Globally Normalising the Transducer for Streaming Speech Recognition
Instead, this paper proposes to approximate the loss function, allowing global normalisation to apply to a state-of-the-art streaming model.
pfl-research: simulation framework for accelerating research in Private Federated Learning
Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to the server and other participants.
