Church: a language for generative models. - Noah D. Goodman; Vikash K. Mansinghka; Daniel M. Roy 001; Kallista A. Bonawitz; Joshua B. Tenenbaum (UAI, 2008)

Composable probabilistic inference with BLAISE. - Kallista A. Bonawitz (2008)

Discrete Distribution Estimation under Local Privacy. - Peter Kairouz; Kallista A. Bonawitz; Daniel Ramage (ICML, 2016)

Practical Secure Aggregation for Federated Learning on User-Held Data. - Kallista A. Bonawitz; Vladimir Ivanov; Ben Kreuter; Antonio Marcedone; H. Brendan McMahan; Sarvar Patel; Daniel Ramage; Aaron Segal; Karn Seth (CoRR, 2016)

Practical Secure Aggregation for Privacy Preserving Machine Learning. - Kallista A. Bonawitz; Vladimir Ivanov; Ben Kreuter; Antonio Marcedone; H. Brendan McMahan; Sarvar Patel; Daniel Ramage; Aaron Segal; Karn Seth (IACR Cryptol. ePrint Arch., 2017)

Federated Learning with Autotuned Communication-Efficient Secure Aggregation. - Kallista A. Bonawitz; Fariborz Salehi; Jakub Konecný; Brendan McMahan; Marco Gruteser (ACSSC, 2019)

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)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Salim El Rouayheb; et al. (CoRR, 2019)

Context Aware Local Differential Privacy. - Jayadev Acharya; Kallista A. Bonawitz; Peter Kairouz; Daniel Ramage; Ziteng Sun (ICML, 2020)

Secure Single-Server Aggregation with (Poly)Logarithmic Overhead. - James Bell 001; Kallista A. Bonawitz; Adrià Gascón; Tancrède Lepoint; Mariana Raykova 001 (IACR Cryptol. ePrint Arch., 2020)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Hubert Eichner; et al. (Found. Trends Mach. Learn., 2021)

Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data. - Kallista A. Bonawitz; Peter Kairouz; Brendan McMahan; Daniel Ramage (ACM Queue, 2021)

Federated learning and privacy. - Kallista A. Bonawitz; Peter Kairouz; Brendan McMahan; Daniel Ramage (Commun. ACM, 2022)

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning. - Zachary Charles; Kallista A. Bonawitz; Stanislav Chiknavaryan; Brendan McMahan; Blaise Agüera y Arcas (CoRR, 2022)

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation. - Eugene Bagdasaryan; Peter Kairouz; Stefan Mellem; Adrià Gascón; Kallista A. Bonawitz; Deborah Estrin; Marco Gruteser (Proc. Priv. Enhancing Technol., 2022)

Confidential Federated Computations. - Hubert Eichner; Daniel Ramage; Kallista A. Bonawitz; Dzmitry Huba; Tiziano Santoro; Brett McLarnon; Timon Van Overveldt; Nova Fallen; Peter Kairouz; Albert Cheu; Katharine Daly; Adrià Gascón; et al. (CoRR, 2024)