Federated Learning for Mobile Keyboard Prediction. - Andrew Hard; Kanishka Rao; Rajiv Mathews; Françoise Beaufays; Sean Augenstein; Hubert Eichner; Chloé Kiddon; Daniel Ramage (CoRR, 2018)

Training Keyword Spotting Models on Non-IID Data with Federated Learning. - Andrew Hard; Kurt Partridge; Cameron Nguyen; Niranjan Subrahmanya; Aishanee Shah; Pai Zhu; Ignacio López-Moreno; Rajiv Mathews (INTERSPEECH, 2020)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift. - Sean Augenstein; Andrew Hard; Kurt Partridge; Rajiv Mathews (CoRR, 2021)

Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. - Chen Zhu 001; Zheng Xu 002; Mingqing Chen; Jakub Konecný; Andrew Hard; Tom Goldstein (ICLR, 2022)

Production federated keyword spotting via distillation, filtering, and joint federated-centralized training. - Andrew Hard; Kurt Partridge; Neng Chen; Sean Augenstein; Aishanee Shah; Hyun Jin Park; Alex Park 001; Sara Ng; Jessica Nguyen; Ignacio López-Moreno; Rajiv Mathews; Françoise Beaufays (INTERSPEECH, 2022)

Mixed Federated Learning: Joint Decentralized and Centralized Learning. - Sean Augenstein; Andrew Hard; Lin Ning 001; Karan Singhal; Satyen Kale; Kurt Partridge; Rajiv Mathews (CoRR, 2022)

Learning from straggler clients in federated learning. - Andrew Hard; Antonious M. Girgis; Ehsan Amid; Sean Augenstein; Lara McConnaughey; Rajiv Mathews; Rohan Anil (CoRR, 2024)

Principled Foundations for Preference Optimization. - Wenxuan Zhou; Shujian Zhang; Brice Magdalou; John Lambert; Ehsan Amid; Richard Nock; Andrew Hard (CoRR, 2025)