Improving On-Device Speaker Verification Using Federated Learning with Privacy. - Filip Granqvist; Matt Seigel; Rogier C. van Dalen; Áine Cahill; Stephen Shum; Matthias Paulik (INTERSPEECH, 2020)

Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications. - Matthias Paulik; Matt Seigel; Henry Mason; Dominic Telaar; Joris Kluivers; Rogier C. van Dalen; Chi Wai Lau; Luke Carlson; Filip Granqvist; Chris Vandevelde; Sudeep Agarwal; Julien Freudiger; et al. (CoRR, 2021)

Training a Tokenizer for Free with Private Federated Learning. - Eugene Bagdasaryan; Congzheng Song; Rogier C. van Dalen; Matt Seigel; Áine Cahill (CoRR, 2022)

Samplable Anonymous Aggregation for Private Federated Data Analysis. - Kunal Talwar; Shan Wang; Audra McMillan; Vojta Jina; Vitaly Feldman; Bailey Basile; Áine Cahill; Yi Sheng Chan; Mike Chatzidakis; Junye Chen; Oliver R. A. Chick; Mona Chitnis; et al. (CoRR, 2023)

Samplable Anonymous Aggregation for Private Federated Data Analysis. - Kunal Talwar; Shan Wang; Audra McMillan; Vitaly Feldman; Pansy Bansal; Bailey Basile; Áine Cahill; Yi Sheng Chan; Mike Chatzidakis; Junye Chen; Oliver R. A. Chick; Mona Chitnis; et al. (CCS, 2024)

Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials. - Jonathan Scott; Áine Cahill (ICML, 2024)

pfl-research: simulation framework for accelerating research in Private Federated Learning. - Filip Granqvist; Congzheng Song; Áine Cahill; Rogier C. van Dalen; Martin Pelikan; Yi Sheng Chan; Xiaojun Feng; Natarajan Krishnaswami; Vojta Jina; Mona Chitnis (NeurIPS, 2024)