Kallista A. Bonawitz

According to our database, Kallista A. Bonawitz authored at least 15 papers between 2008 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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

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

Federated learning and privacy.

Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage

Commun. ACM, 2022

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

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, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

Found. Trends Mach. Learn., 2021

2020

Secure Single-Server Aggregation with (Poly)Logarithmic Overhead.

James Bell, Kallista A. Bonawitz, Adrià Gascón, Tancrède Lepoint, Mariana Raykova

IACR Cryptol. ePrint Arch., 2020

Context Aware Local Differential Privacy.

Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun

Proceedings of the 37th International Conference on Machine Learning, 2020

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, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

CoRR, 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, Daniel Ramage, Jason Roselander

Proceedings of Machine Learning and Systems 2019, 2019

Federated Learning with Autotuned Communication-Efficient Secure Aggregation.

Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2017

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

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

Discrete Distribution Estimation under Local Privacy.

Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage

Proceedings of the 33nd International Conference on Machine Learning, 2016

2008

Composable probabilistic inference with BLAISE.

Kallista A. Bonawitz

PhD thesis, 2008

Church: a language for generative models.

Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum

Proceedings of the UAI 2008, 2008