Improving Federated Learning Personalization via Model Agnostic Meta Learning. - Yihan Jiang; Jakub Konecný; Keith Rush; Sreeram Kannan (CoRR, 2019)

Dimension Independence in Unconstrained Private ERM via Adaptive Preconditioning. - Peter Kairouz; Mónica Ribero; Keith Rush; Abhradeep Thakurta (CoRR, 2020)

(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces. - Peter Kairouz; Mónica Ribero Diaz; Keith Rush; Abhradeep Thakurta (COLT, 2021)

Adaptive Federated Optimization. - Sashank J. Reddi; Zachary Charles; Manzil Zaheer; Zachary Garrett; Keith Rush; Jakub Konecný; Sanjiv Kumar; Hugh Brendan McMahan (ICLR, 2021)

Federated Reconstruction: Partially Local Federated Learning. - Karan Singhal; Hakim Sidahmed; Zachary Garrett; Shanshan Wu; Keith Rush; Sushant Prakash (CoRR, 2021)

Iterated Vector Fields and Conservatism, with Applications to Federated Learning. - Zachary Charles; Keith Rush (ALT, 2022)

Does Federated Dropout actually work? - Gary Cheng 004; Zachary Charles; Zachary Garrett; Keith Rush (CVPR Workshops, 2022)

Private Online Prefix Sums via Optimal Matrix Factorizations. - Brendan McMahan; Keith Rush; Abhradeep Guha Thakurta (CoRR, 2022)

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. - Christopher A. Choquette-Choo; Hugh Brendan McMahan; J. Keith Rush; Abhradeep Guha Thakurta (ICML, 2023)

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. - Anastasia Koloskova; Ryan McKenna; Zachary Charles; John Keith Rush; H. Brendan McMahan (NeurIPS, 2023)

Federated Automatic Differentiation. - Keith Rush; Zachary Charles; Zachary Garrett (CoRR, 2023)

Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. - Anastasia Koloskova; Ryan McKenna; Zachary Charles; Keith Rush; Brendan McMahan (CoRR, 2023)

(Amplified) Banded Matrix Factorization: A unified approach to private training. - Christopher A. Choquette-Choo; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Keith Rush; Abhradeep Guha Thakurta; Zheng Xu 002 (CoRR, 2023)

FAX: Scalable and Differentiable Federated Primitives in JAX. - Keith Rush; Zachary Charles; Zachary Garrett (CoRR, 2024)

Cascade-Aware Training of Language Models. - Congchao Wang; Sean Augenstein; Keith Rush; Wittawat Jitkrittum; Harikrishna Narasimhan; Ankit Singh Rawat; Aditya Krishna Menon; Alec Go (CoRR, 2024)

Fine-Tuning Large Language Models with User-Level Differential Privacy. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (CoRR, 2024)

Learning with User-Level Differential Privacy Under Fixed Compute Budgets. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (SaTML, 2025)

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch. - Arthur Douillard; Yanislav Donchev; Keith Rush; Satyen Kale; Zachary Charles; Zachary Garrett; Gabriel Teston; Dave Lacey; Ross McIlroy; Jiajun Shen; Alexandre Ramé; Arthur Szlam; et al. (CoRR, 2025)

Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo. - Zachary Charles; Gabriel Teston; Lucio Dery; Keith Rush; Nova Fallen; Zachary Garrett; Arthur Szlam; Arthur Douillard (CoRR, 2025)

Correlated Noise Mechanisms for Differentially Private Learning. - Krishna Pillutla; Jalaj Upadhyay; Christopher A. Choquette-Choo; Krishnamurthy Dvijotham; Arun Ganesh; Monika Henzinger; Jonathan Katz; Ryan McKenna; H. Brendan McMahan; Keith Rush; Thomas Steinke 002; Abhradeep Thakurta (CoRR, 2025)