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07/18/2023
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
We introduce a library, Dataset Grouper, to create large-scale group-str...
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02/02/2023
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning
We study stochastic optimization with linearly correlated noise. Our stu...
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01/18/2023
Federated Automatic Differentiation
Federated learning (FL) is a general framework for learning across heter...
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08/19/2022
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
Federated learning (FL) is a framework for machine learning across heter...
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06/18/2022
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
Personalized federated learning considers learning models unique to each...
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01/07/2022
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
A significant bottleneck in federated learning is the network communicat...
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09/08/2021
Iterated Vector Fields and Conservatism, with Applications to Federated Learning
We study iterated vector fields and investigate whether they are conserv...
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06/15/2021
On Large-Cohort Training for Federated Learning
Federated learning methods typically learn a model by iteratively sampli...
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06/04/2021
Local Adaptivity in Federated Learning: Convergence and Consistency
The federated learning (FL) framework trains a machine learning model us...
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03/08/2021
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
We study a family of algorithms, which we refer to as local update metho...
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07/02/2020
On the Outsized Importance of Learning Rates in Local Update Methods
We study a family of algorithms, which we refer to as local update metho...
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02/29/2020
Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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10/18/2019
Improving the convergence of SGD through adaptive batch sizes
Mini-batch stochastic gradient descent (SGD) approximates the gradient o...
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07/29/2019
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
To improve the resilience of distributed training to worst-case, or Byza...
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05/22/2019
Convergence and Margin of Adversarial Training on Separable Data
Adversarial training is a technique for training robust machine learning...
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05/08/2019
Does Data Augmentation Lead to Positive Margin?
Data augmentation (DA) is commonly used during model training, as it sig...
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01/28/2019
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding
We present ErasureHead, a new approach for distributed gradient descent ...
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11/08/2018
A Geometric Perspective on the Transferability of Adversarial Directions
State-of-the-art machine learning models frequently misclassify inputs t...
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06/11/2018
ATOMO: Communication-efficient Learning via Atomic Sparsification
Distributed model training suffers from communication overheads due to f...
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05/25/2018
Gradient Coding via the Stochastic Block Model
Gradient descent and its many variants, including mini-batch stochastic ...
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03/27/2018
DRACO: Robust Distributed Training via Redundant Gradients
Distributed model training is vulnerable to worst-case system failures a...
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11/17/2017
Approximate Gradient Coding via Sparse Random Graphs
Distributed algorithms are often beset by the straggler effect, where th...
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10/23/2017
Stability and Generalization of Learning Algorithms that Converge to Global Optima
We establish novel generalization bounds for learning algorithms that co...
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07/08/2017