2 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 2 cited
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…
eess.AS2020★ 2 cited
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…