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ORCIDKallista A. Bonawitz
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- affiliation: Google, New York, NY, USA
- affiliation (PhD 2008): Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
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2020 – today
- 2024
[i12]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, Marco Gruteser, Brendan McMahan:
Confidential Federated Computations. CoRR abs/2404.10764 (2024)- 2022
- [j4]Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage:
Federated learning and privacy. Commun. ACM 65(4): 90-97 (2022) - [j3]Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem
, Adrià Gascón, Kallista A. Bonawitz, Deborah Estrin, Marco Gruteser:
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation. Proc. Priv. Enhancing Technol. 2022(4): 162-182 (2022) - [i11]Zachary Charles, Kallista A. Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Agüera y Arcas:
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning. CoRR abs/2208.09432 (2022) - 2021
- [j2]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:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [j1]Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage:
Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data. ACM Queue 19(5): 87-114 (2021) - [i10]Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem, Adrià Gascón, Kallista A. Bonawitz, Deborah Estrin, Marco Gruteser:
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation. CoRR abs/2111.02356 (2021) - 2020
- [c7]James Henry Bell, Kallista A. Bonawitz, Adrià Gascón, Tancrède Lepoint, Mariana Raykova:
Secure Single-Server Aggregation with (Poly)Logarithmic Overhead. CCS 2020: 1253-1269 - [c6]Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun:
Context Aware Local Differential Privacy. ICML 2020: 52-62 - [i9]James Bell, Kallista A. Bonawitz, Adrià Gascón, Tancrède Lepoint, Mariana Raykova:
Secure Single-Server Aggregation with (Poly)Logarithmic Overhead. IACR Cryptol. ePrint Arch. 2020: 704 (2020)
2010 – 2019
- 2019
- [c5]Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser:
Federated Learning with Autotuned Communication-Efficient Secure Aggregation. ACSSC 2019: 1222-1226 - [c4]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:
Towards Federated Learning at Scale: System Design. SysML 2019 - [i8]Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander:
Towards Federated Learning at Scale: System Design. CoRR abs/1902.01046 (2019) - [i7]Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun:
Context-Aware Local Differential Privacy. CoRR abs/1911.00038 (2019) - [i6]Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser:
Federated Learning with Autotuned Communication-Efficient Secure Aggregation. CoRR abs/1912.00131 (2019) - [i5]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:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2017
- [c3]Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth:
Practical Secure Aggregation for Privacy-Preserving Machine Learning. CCS 2017: 1175-1191 - [i4]Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth:
Practical Secure Aggregation for Privacy Preserving Machine Learning. IACR Cryptol. ePrint Arch. 2017: 281 (2017) - 2016
- [c2]Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage:
Discrete Distribution Estimation under Local Privacy. ICML 2016: 2436-2444 - [i3]Peter Kairouz, Kallista A. Bonawitz, Daniel Ramage:
Discrete Distribution Estimation under Local Privacy. CoRR abs/1602.07387 (2016) - [i2]Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth:
Practical Secure Aggregation for Federated Learning on User-Held Data. CoRR abs/1611.04482 (2016) - 2012
- [i1]Noah D. Goodman, Vikash Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models. CoRR abs/1206.3255 (2012)
2000 – 2009
- 2008
- [b1]Kallista A. Bonawitz:
Composable probabilistic inference with BLAISE. Massachusetts Institute of Technology, Cambridge, MA, USA, 2008 - [c1]Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models. UAI 2008: 220-229
Coauthor Index
aka: Brendan McMahan
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