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FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Federated Learning (FL) has been gaining significant traction across dif...
02/26/2021 ∙ by Samuel Horvath, et al. ∙
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Hyperparameter Transfer Learning with Adaptive Complexity
Bayesian optimization (BO) is a sample efficient approach to automatical...
02/25/2021 ∙ by Samuel Horvath, et al. ∙
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Optimal Client Sampling for Federated Learning
It is well understood that client-master communication can be a primary ...
10/26/2020 ∙ by Wenlin Chen, et al. ∙
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Lower Bounds and Optimal Algorithms for Personalized Federated Learning
In this work, we consider the optimization formulation of personalized f...
10/05/2020 ∙ by Filip Hanzely, et al. ∙
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A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
Modern large-scale machine learning applications require stochastic opti...
06/19/2020 ∙ by Samuel Horvath, et al. ∙
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On Biased Compression for Distributed Learning
In the last few years, various communication compression techniques have...
02/27/2020 ∙ by Aleksandr Beznosikov, et al. ∙
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Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Adaptivity is an important yet under-studied property in modern optimiza...
02/13/2020 ∙ by Samuel Horvath, et al. ∙
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Natural Compression for Distributed Deep Learning
Due to their hunger for big data, modern deep learning models are traine...
05/27/2019 ∙ by Samuel Horvath, et al. ∙
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Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
The stochastic variance-reduced gradient method (SVRG) and its accelerat...
01/24/2019 ∙ by Dmitry Kovalev, et al. ∙
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