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)
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The federated learning paradigm has motivated the development of methods for aggregating multiple client updates into a global server model, without sharing client data. Many federated learning algorithms, including the canonical Federated Averaging (FedAvg), take a direct (possibly weighted) average of the client parameter updates, motivated by results in distributed optimization. In this work, we adopt a function space perspective and propose a new algorithm, FedFish, that aggregates local approximations to the functions learned by clients, using an estimate based on their Fisher information. We evaluate FedFish on realistic, large-scale cross-device benchmarks. While the performance of FedAvg can suffer as client models drift further apart, we demonstrate that FedFish is more robust to longer local training. Our evaluation across several settings in image and language benchmarks shows that FedFish outperforms FedAvg as local training epochs increase. Further, FedFish results in global networks that are more amenable to efficient personalization via local fine-tuning on the same or shifted data distributions. For instance, federated pretraining on the C4 dataset, followed by few-shot personalization on Stack Overflow, results in a 7% improvement in next-token prediction by FedFish over FedAvg.
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Compressing model updates is critical for reducing communication costs in federated learning. We examine the problem using rate--distortion theory to present a compression method that is near-optimal in many use cases. We empirically show that common transforms applied to model updates in standard compression algorithms, normalization in QSGD and random rotation in DRIVE, yield sub-optimal compressed representations in practice.
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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)
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We introduce Dataset Grouper, a library to create large-scale group-structured (e.g., federated) datasets, enabling federated learning simulation at the scale of foundation models. This library facilitates the creation of group-structured versions of existing datasets based on user-specified partitions and directly leads to a variety of useful heterogeneous datasets that can be plugged into existing software frameworks. Dataset Grouper offers three key advantages. First, it scales to settings where even a single group's dataset is too large to fit in memory. Second, it provides flexibility, both in choosing the base (non-partitioned) dataset and in defining partitions. Finally, it is framework-agnostic. We empirically demonstrate that Dataset Grouper enables large-scale federated language modeling simulations on datasets that are orders of magnitude larger than in previous work, allowing for federated training of language models with hundreds of millions, and even billions, of parameters. Our experimental results show that algorithms like FedAvg operate more as meta-learning methods than as empirical risk minimization methods at this scale, suggesting their utility in downstream personalization and task-specific adaptation. Dataset Grouper is available at https://github.com/google-research/dataset_grouper.
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Model sizes are limited in Federated Learning due to communication bandwidth constraints and on-device memory constraints. The success of scaling model sizes in other machine learning domains, especially when it comes to generalizing to new data distributions, motivates the development of methods of training large scale models in Federated Learning. Inspired by dropout, [3] proposed Federated Dropout as a way of scaling up model sizes: clients train randomly selected subsets of the larger server model. In spite of the promising empirical results and the many other works that build on it [1, 8, 13], we argue in this paper that the metrics used to measure performance of Federated Dropout and its variants are misleading. We propose and perform new experiments which suggest that Federated Dropout is actually detrimental to scaling efforts. We show how a simple ensembling technique outperforms Federated Dropout and other baselines. We perform ablations which suggest that the best performing variations of Federated Dropout attempt to approximate ensembling. The simplicity of ensembling allows for easy, practical implementations. Furthermore, our ensembling technique naturally leverages the parallelizable nature of Federated Learning—recall that it is easy to train several models independently because there are a lot of clients and server-compute is not the bottleneck. Ensembling’s strong performance against our baselines suggests that Federated Learning models may be more easily scaled than previously thought e.g., via boosting.
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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)
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We study a family of algorithms, which we refer to as local update methods, generalizing many federated and meta-learning algorithms. We prove that for quadratic models, local update methods are equivalent to first-order optimization on a surrogate loss we exactly characterize. Moreover, fundamental algorithmic choices (such as learning rates) explicitly govern a trade-off between the condition number of the surrogate loss and its alignment with the true loss. We derive novel convergence rates showcasing these trade-offs and highlight their importance in communication-limited settings. Using these insights, we are able to compare local update methods based on their convergence/accuracy trade-off, not just their convergence to critical points of the empirical loss. Our results shed new light on a broad range of phenomena, including the efficacy of server momentum in federated learning and the impact of proximal client updates.
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The federated learning (FL) framework trains a machine learning model using decentralized data stored at edge client devices by periodically aggregating locally trained models. Popular optimization algorithms of FL use vanilla (stochastic) gradient descent for both local updates at clients and global updates at the aggregating server. Recently, adaptive optimization methods such as AdaGrad have been studied for server updates. However, the effect of using adaptive optimization methods for local updates at clients is not yet understood. We show in both theory and practice that while local adaptive methods can accelerate convergence, they can cause a non-vanishing solution bias, where the final converged solution may be different from the stationary point of the global objective function. We propose correction techniques to overcome this inconsistency and complement the local adaptive methods for FL. Extensive experiments on realistic federated training tasks show that the proposed algorithms can achieve faster convergence and higher test accuracy than the baselines without local adaptivity.
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On Large-Cohort Training for Federated Learning
Sergei Shmulyian
Virginia Smith
Advances in Neural Information Processing Systems (2021)
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Federated learning methods typically learn a model by iteratively sampling updates from a population of clients. In this work, we explore how the number of clients sampled at each round (the cohort size) impacts the quality of the learned model and the training dynamics of federated learning algorithms. Our work poses three fundamental questions. First, what challenges arise when trying to scale federated learning to larger cohorts? Second, what parallels exist between cohort sizes in federated learning and batch sizes in centralized learning? Last, how can we design federated learning methods that effectively utilize larger cohort sizes? We give partial answers to these questions based on extensive empirical evaluation. Our work highlights a number of challenges stemming from the use of larger cohorts. While some of these (such as generalization issues and diminishing returns) are analogs of large-batch training challenges, others (including training failures and fairness concerns) are unique to federated learning.
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Adaptive Federated Optimization
Jakub Konečný
(2021)
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Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Due to the heterogeneity of the client datasets, standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable convergence behavior. In non-federated settings, adaptive optimization methods have had notable success in combating such issues. In this work, we propose federated versions of adaptive optimizers, including Adagrad, Yogi and Adam, and analyze their convergence in the presence of heterogeneous data for general nonconvex settings. Our results highlight the interplay between client heterogeneity and communication efficiency. We also perform extensive experiments on these methods and show that the use of adaptive optimizers can improve the performance of federated learning.
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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
Mi Zhang
Tong Zhang
Chunxiang (Jake) Zheng
Chen Zhu
arxiv (2021)
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Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.
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Advances and Open Problems in Federated Learning
Brendan Avent
Aurélien Bellet
Mehdi Bennis
Arjun Nitin Bhagoji
Graham Cormode
Rachel Cummings
Rafael G.L. D'Oliveira
Salim El Rouayheb
David Evans
Josh Gardner
Adrià Gascón
Phillip B. Gibbons
Marco Gruteser
Zaid Harchaoui
Chaoyang He
Lie He
Zhouyuan Huo
Justin Hsu
Martin Jaggi
Tara Javidi
Gauri Joshi
Mikhail Khodak
Jakub Konečný
Aleksandra Korolova
Farinaz Koushanfar
Sanmi Koyejo
Tancrède Lepoint
Yang Liu
Prateek Mittal
Richard Nock
Ayfer Özgür
Rasmus Pagh
Ramesh Raskar
Dawn Song
Weikang Song
Sebastian U. Stich
Ziteng Sun
Florian Tramèr
Praneeth Vepakomma
Jianyu Wang
Li Xiong
Qiang Yang
Felix X. Yu
Han Yu
Arxiv (2019)
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Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and mitigates many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents a comprehensive list of open problems and challenges.
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