4 citations · 8 across the 5 of their papers we have counts for
5 papers
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
Jonathan Scott, Áine Cahill
In practice, training using federated learning can be orders of magnitude slower than standard centralized training. This severely limits the amount of experimentation and tuning t…
pfl-research: simulation framework for accelerating research in Private Federated Learning
Filip Granqvist, Congzheng Song, Áine Cahill +7
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 t…
Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar, Shan Wang, Audra McMillan +34
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private…
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik, Matt Seigel, Henry Mason +19
We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML sy…
Improving on-device speaker verification using federated learning with privacy
Filip Granqvist, Matt Seigel, Rogier van Dalen +3
Information on speaker characteristics can be useful as side information in improving speaker recognition accuracy. However, such information is often private. This paper investiga…