I am a research scientist at Google working on federated learning. I received a Ph.D. in applied mathematics from the University of Wisconsin-Madison, and went on to do a postdoc with the wonderful Dimitris Papailiopoulos.

I am interested in practical and efficient optimization methods for machine learning, especially in distributed settings.

I care deeply about open-source software. I actively contribute to and develop multiple libraries for distributed and federated learning experimentation, including TensorFlow Federated, Dataset Grouper, and DrJAX.

In my spare time, I foster dogs and cats, and participate in a Flyball team. I also enjoy baking. You can find recipes I am fond of in my dissertation (no, really).

Publications & Preprints

2024

• Fine-Tuning Large Language Models with User-Level Differential Privacy
  Z. Charles, A. Ganesh, R. McKenna, B. McMahan, N. Mitchell, K. Pillutla, K. Rush. TF2M@ICML 2024 Workshop.

• DrJAX: Scalable and Differentiable MapReduce Primitives in JAX
  K. Rush, Z. Charles, Z. Garrett., S. Augenstein, N. Mitchell. WANT@ICML 2024 Workshop.

• Federated Automatic Differentiation
  K. Rush, Z. Charles, Z. Garrett. To appear, JMLR.

• Leveraging Function Space Aggregation for Federated Learning at Scale
  N. Dhawan, N. Mitchell, Z. Charles, Z. Garrett, K. Dziugaite. TMLR 2024.

2023

• Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
  Z. Charles, N. Mitchell, K. Pillutla, M. Reneer, Z. Garrett. NeurIPS 2023.

• Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning
  A. Koloskova, R. McKenna, Z. Charles, K. Rush, B. McMahan. NeurIPS 2023.

• A Rate-Distortion View on Model Updates
  N. Mitchell, J. Ballé, Z. Charles, J. Konečný. ICLR Tiny Papers, 2023.

2022

• Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
  Z. Charles, K. Bonawitz, S. Chiknavaryan, B. McMahan, B. Agüera y Arcas.

• Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
  S. Wu, T. Li, Z. Charles, Y. Xiao, Z. Liu, Z. Xu, V. Smith.

• Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
  N. Mitchell, J. Ballé, Z. Charles, J. Konečný.

• Iterated Vector Fields and Conservatism, with Applications to Federated Learning
  Z. Charles, K. Rush. ALT 2022.

2021

• A Field Guide to Federated Optimization
  J. Wang, Z. Charles, Z. Xu, G. Joshi, H. B. McMahan, et al.

• On Large-Cohort Training for Federated Learning
  Z. Charles, Z. Garrett, Z. Huo, S. Shmulyian, V. Smith. NeurIPS 2021.

• Local Adaptivity in Federated Learning: Convergence and Consistency
  J. Wang, Z. Xu, Z. Garrett, Z. Charles, L. Liu, G. Joshi.

• Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
  Z. Charles and J. Konečný. AISTATS 2021.

• Adaptive Federated Optimization
  S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný, S. Kumar, H. B. McMahan. ICLR 2021.

2020

• Advances and Open Problems in Federated Learning
  P. Kairouz, H. B. McMahan, et al. (including Z. Charles).

• On the Outsized Importance of Learning Rates in Local Update Methods
  Z. Charles and J. Konečný.

2019

• Convergence and Margin of Adversarial Training on Separable Data
  Z. Charles, S. Rajput, S. Wright, D. Papailiopoulos.

• DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation (arXiv)
  S. Rajput, H. Wang, Z. Charles, D. Papailiopoulos. NeurIPS 2019.

• Does Data Augmentation Lead to Positive Margin? (arXiv)
  S. Rajput, Z. Feng, Z. Charles, P. Loh, D. Papailiopoulos. ICML, 2019.

• A Geometric Perspective on the Transferability of Adversarial Directions (arXiv)
  Z. Charles, H. Rosenberg, D. Papailiopoulos. AISTATS, 2019.

• ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Codes
  H. Wang, Z. Charles, D. Papailiopoulos.

2018

• ATOMO: Communication-efficient Learning via Atomic Sparsification (arXiv)
  H. Wang, S. Sievert, Z. Charles, S. Liu, S. Wright, D. Papailiopoulos. NeurIPS, 2018.

• Stability and Generalization of Learning Algorithms that Converge to Global Optima (arXiv)
  Z. Charles and D. Papailiopoulos. ICML, 2018.

• Approximate Gradient Coding via Sparse Random Graphs (arXiv)
  Z. Charles, D. Papailiopoulos, J. Ellenberg.

• DRACO: Robust Distributed Training via Redundant Gradients (arXiv)
  L. Chen, H. Wang, Z. Charles, D. Papailiopoulos. ICML, 2018.

• Gradient Coding Using the Stochastic Block Model (arXiv)
  Z. Charles and D. Papailiopoulos. ISIT, 2018.

• Subspace Clustering with Missing and Corrupted Data (arXiv)
  Z. Charles, A. Jalali, R. Willett. IEEE Data Science Workshop, 2018.

• Exploiting Algebraic Structure in Global Optimization and the Belgian Chocolate Problem (arXiv)
  Z. Charles and N. Boston. Journal of Global Optimization, 2018.

• Generating Random Factored Ideals in Number Fields (arXiv)
  Z. Charles. Mathematics of Computation, 2018.

2017 and earlier

• Algebraic and Geometric Structure in Machine Learning and Optimization Algorithms (link)
  Z. Charles. Ph.D. Thesis, University of Wisconsin-Madison, Dec 2017.

• Efficiently Finding All Power Flow Solutions to Tree Networks
  A. Zachariah and Z. Charles. Allerton, 2017.

• Nonpositive Eigenvalues of Hollow, Symmetric, Nonnegative Matrices (arXiv)
  Z. Charles, M. Farber, C. R. Johnson, L. Kennedy-Shaffer. SIAM Journal on Matrix Analysis and Applications, 2013.

• Nonpositive Eigenvalues of the Adjacency Matrix and Lower Bounds for Laplacian Eigenvalues (arXiv)
  Z. Charles, M. Farber, C. R. Johnson, L. Kennedy-Shaffer. Discrete Mathematics, 2013.

• The Relation Between the Diagonal Entries and the Eigenvalues of a Symmetric Matrix, Based upon the Sign Pattern of its Off-Diagonal Entries
  Z. Charles, M. Farber, C. R. Johnson, L. Kennedy-Shaffer. Linear Algebra and its Applications, 2013.