30 citations · 35 across the 4 of their papers we have counts for
4 papers
Training a Tokenizer for Free with Private Federated Learning
Eugene Bagdasaryan, Congzheng Song, Rogier van Dalen +2
Federated learning with differential privacy, i.e. private federated learning (PFL), makes it possible to train models on private data distributed across users' devices without har…
Enforcing fairness in private federated learning via the modified method of differential multipliers
Borja Rodríguez-Gálvez, Filip Granqvist, Rogier van Dalen +1
Federated learning with differential privacy, or private federated learning, provides a strategy to train machine learning models while respecting users' privacy. However, differen…
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…