Zachary Charles

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 32

Top Topics

Correlated Noise

Differential Privacy

Differentially Private

Top Venues

J. Glob. Optim.

Publications

Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR (2024)
Keith Rush, Zachary Charles, Zachary Garrett
FAX: Scalable and Differentiable Federated Primitives in JAX. CoRR (2024)
Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
Leveraging Function Space Aggregation for Federated Learning at Scale. Trans. Mach. Learn. Res. 2024 (2024)
Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. NeurIPS (2023)
Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR (2023)
Keith Rush, Zachary Charles, Zachary Garrett
Federated Automatic Differentiation. CoRR (2023)
Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
Leveraging Function Space Aggregation for Federated Learning at Scale. CoRR (2023)
Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný
A Rate-Distortion View on Model Updates. Tiny Papers @ ICLR (2023)
Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS (2023)
Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. CoRR (2023)
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 (2022)
Zachary Charles, Keith Rush
Iterated Vector Fields and Conservatism, with Applications to Federated Learning. ALT (2022)
Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ziyu Liu, Zheng Xu, Virginia Smith
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning. CoRR (2022)
Gary Cheng, Zachary Charles, Zachary Garrett, Keith Rush
Does Federated Dropout actually work? CVPR Workshops (2022)
Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory. CoRR (2022)
Zachary Charles, Jakub Konecný
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. AISTATS (2021)
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, Gauri Joshi
Local Adaptivity in Federated Learning: Convergence and Consistency. CoRR (2021)
Zachary Charles, Jakub Konecný
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. CoRR (2021)
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan
Adaptive Federated Optimization. ICLR (2021)
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
On Large-Cohort Training for Federated Learning. NeurIPS (2021)
Zachary Charles, Keith Rush
Iterated Vector Fields and Conservatism, with Applications to Federated Learning. CoRR (2021)
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) (2021)
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
On Large-Cohort Training for Federated Learning. CoRR (2021)
Zachary Charles, Jakub Konecný
On the Outsized Importance of Learning Rates in Local Update Methods. CoRR (2020)
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan
Adaptive Federated Optimization. CoRR (2020)
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 (2019)
Zachary Charles, Nigel Boston
Exploiting algebraic structure in global optimization and the Belgian chocolate problem. J. Glob. Optim. 72 (2) (2018)
Alisha Zachariah, Zachary Charles, Nigel Boston, Bernard C. Lesieutre
Distributions of the Number of Solutions to the Network Power Flow Equations. ISCAS (2018)
Zachary Charles
Generating random factored ideals in number fields. Math. Comput. 87 (312) (2018)
Zachary Charles, Amin Jalali, Rebecca Willett
Sparse Subspace Clustering with Missing and Corrupted Data. DSW (2018)
Alisha Zachariah, Zachary Charles
Efficiently finding all power flow solutions to tree networks. Allerton (2017)