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06/05/2023
Improving Accelerated Federated Learning with Compression and Importance Sampling
Federated Learning is a collaborative training framework that leverages ...
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05/25/2023
A Guide Through the Zoo of Biased SGD
Stochastic Gradient Descent (SGD) is arguably the most important single ...
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02/20/2023
TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation
In federated learning, a large number of users are involved in a global ...
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12/29/2022
Can 5th Generation Local Training Methods Support Client Sampling? Yes!
The celebrated FedAvg algorithm of McMahan et al. (2017) is based on thr...
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09/16/2022
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization
In this paper, we propose a new zero order optimization method called mi...
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05/08/2022
Federated Random Reshuffling with Compression and Variance Reduction
Random Reshuffling (RR), which is a variant of Stochastic Gradient Desce...
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02/18/2022
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
We introduce ProxSkip – a surprisingly simple and provably efficient met...
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01/26/2022
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization
We present a theoretical study of server-side optimization in federated ...
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04/19/2021
Random Reshuffling with Variance Reduction: New Analysis and Better Rates
Virtually all state-of-the-art methods for training supervised machine l...
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10/02/2020
Distributed Proximal Splitting Algorithms with Rates and Acceleration
We analyze several generic proximal splitting algorithms well suited for...
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04/03/2020
