Towards Federated Learning at Scale: System Design. - Kallista A. Bonawitz; Hubert Eichner; Wolfgang Grieskamp; Dzmitry Huba; Alex Ingerman; Vladimir Ivanov; Chloé Kiddon; Jakub Konecný; Stefano Mazzocchi; Brendan McMahan; Timon Van Overveldt; David Petrou; et al. (SysML, 2019)

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. - Miles Brundage; Shahar Avin; Jasmine Wang; Haydn Belfield; Gretchen Krueger; Gillian K. Hadfield; Heidy Khlaaf; Jingying Yang; Helen Toner; Ruth Fong; Tegan Maharaj; Pang Wei Koh; et al. (CoRR, 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)

Privacy-first health research with federated learning. - Adam Sadilek; Luyang Liu; Dung Nguyen 002; Methun Kamruzzaman; Stylianos Serghiou; Benjamin Rader; Alex Ingerman; Stefan Mellem; Peter Kairouz; Elaine O. Nsoesie; Jamie Macfarlane; Anil Vullikanti; et al. (npj Digit. Medicine, 2021)

The Ethics of Advanced AI Assistants. - Iason Gabriel; Arianna Manzini; Geoff Keeling; Lisa Anne Hendricks; Verena Rieser; Hasan Iqbal; Nenad Tomasev; Ira Ktena; Zachary Kenton; Mikel Rodriguez; Seliem El-Sayed; Sasha Brown; et al. (CoRR, 2024)