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08/28/2023
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Deep Neural Networks (DNNs) have been a large driver and enabler for AI ...
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05/30/2023
Clip21: Error Feedback for Gradient Clipping
Motivated by the increasing popularity and importance of large-scale tra...
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05/29/2023
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees
Efficient distributed training is a principal driver of recent advances ...
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05/29/2023
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity
We present a partially personalized formulation of Federated Learning (F...
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04/11/2023
Improving Performance of Private Federated Models in Medical Image Analysis
Federated learning (FL) is a distributed machine learning (ML) approach ...
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12/07/2022
Partial Disentanglement with Partially-Federated GANs (PaDPaF)
Federated learning has become a popular machine learning paradigm with m...
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08/10/2022
Adaptive Learning Rates for Faster Stochastic Gradient Methods
In this work, we propose new adaptive step size strategies that improve ...
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08/07/2022
Granger Causality using Neural Networks
The Granger Causality (GC) test is a famous statistical hypothesis test ...
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07/01/2022
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity
Federated learning (FL) is an emerging machine learning paradigm involvi...
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06/01/2022
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top
Byzantine-robustness has been gaining a lot of attention due to the grow...
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04/27/2022
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
The practice of applying several local updates before aggregation across...
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02/07/2022
FL_PyTorch: optimization research simulator for federated learning
Federated Learning (FL) has emerged as a promising technique for edge de...
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11/22/2021
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
Federated Learning (FL) is an increasingly popular machine learning para...
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02/26/2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Federated Learning (FL) has been gaining significant traction across dif...
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02/25/2021
Hyperparameter Transfer Learning with Adaptive Complexity
Bayesian optimization (BO) is a sample efficient approach to automatical...
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10/26/2020
Optimal Client Sampling for Federated Learning
It is well understood that client-master communication can be a primary ...
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10/05/2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
In this work, we consider the optimization formulation of personalized f...
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06/19/2020
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
Modern large-scale machine learning applications require stochastic opti...
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02/27/2020
On Biased Compression for Distributed Learning
In the last few years, various communication compression techniques have...
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02/13/2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Adaptivity is an important yet under-studied property in modern optimiza...
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05/27/2019
Natural Compression for Distributed Deep Learning
Due to their hunger for big data, modern deep learning models are traine...
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01/24/2019
