Search Results for author:
Found 7 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.
FLAIR: Federated Learning Annotated Image Repository
We believe FLAIR can serve as a challenging benchmark for advancing the state-of-the art in federated learning.
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.
Samplable Anonymous Aggregation for Private Federated Data Analysis
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data.
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.
