Zachary Charles
Researcher in federated optimization and federated learning. Interested in distributed learning, communication-efficient learning, robustness, fairness, and applied mathematics. Received a PhD in applied mathematics from the University of Wisconsin-Madison.
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Leveraging Function Space Aggregation for Federated Learning at Scale
Nikita Dhawan
Karolina Dziugaite
Transactions on Machine Learning Research (2024)
Federated Automatic Differentiation
Journal of Machine Learning Research (JMLR), 25 (2024), pp. 1-39
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
Krishna Pillutla
Michael Reneer
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks (2023)
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
Jakub Konečný
International Conference on Artificial Intelligence and Statistics (2021), pp. 2575-2583 (to appear)
A Field Guide to Federated Optimization
Jianyu Wang
Gauri Joshi
Maruan Al-Shedivat
Galen Andrew
A. Salman Avestimehr
Katharine Daly
Deepesh Data
Suhas Diggavi
Hubert Eichner
Advait Gadhikar
Antonious M. Girgis
Filip Hanzely
Chaoyang He
Samuel Horvath
Martin Jaggi
Tara Javidi
Satyen Chandrakant Kale
Sai Praneeth Karimireddy
Jakub Konečný
Sanmi Koyejo
Tian Li
Peter Richtarik
Karan Singhal
Virginia Smith
Mahdi Soltanolkotabi
Weikang Song
Sebastian Stich
Ameet Talwalkar
Hongyi Wang
Blake Woodworth
Honglin Yuan
Manzil Zaheer
Mi Zhang
Tong Zhang
Chunxiang (Jake) Zheng
Chen Zhu
arxiv (2021)
Adaptive Federated Optimization
Manzil Zaheer
Jakub Konečný
(2021)
On Large-Cohort Training for Federated Learning
Sergei Shmulyian
Virginia Smith
Advances in Neural Information Processing Systems (2021)
