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Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning

Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning

Grigory Malinovsky
Kai Yi
Peter Richtárik
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Papers citing "Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning"

10 / 10 papers shown
Title
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
Jiahao Xu
Zikai Zhang
Rui Hu
AAML
FedML
Presented at ResearchTrend Connect | FedML on
152
2
0
11 Mar 2025
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
Laurent Condat
A. Maranjyan
Peter Richtárik
49
4
0
07 Mar 2024
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
Siqi Zhang
S. Choudhury
Sebastian U. Stich
Nicolas Loizou
FedML
19
3
0
08 Jun 2023
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime
Jongho Park
Jinchao Xu
FedML
60
1
0
17 May 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
Aleksandr Beznosikov
Martin Takáč
Alexander Gasnikov
31
10
0
15 Feb 2023
An Optimal Algorithm for Strongly Convex Min-min Optimization
An Optimal Algorithm for Strongly Convex Min-min Optimization
Alexander Gasnikov
D. Kovalev
Grigory Malinovsky
32
1
0
29 Dec 2022
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
A. Maranjyan
M. Safaryan
Peter Richtárik
34
13
0
28 Oct 2022
A Field Guide to Federated Optimization
A Field Guide to Federated Optimization
Jianyu Wang
Zachary B. Charles
Zheng Xu
Gauri Joshi
H. B. McMahan
...
Mi Zhang
Tong Zhang
Chunxiang Zheng
Chen Zhu
Wennan Zhu
FedML
187
412
0
14 Jul 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
A. Mitra
Rayana H. Jaafar
George J. Pappas
Hamed Hassani
FedML
55
157
0
14 Feb 2021
Linearly Converging Error Compensated SGD
Linearly Converging Error Compensated SGD
Eduard A. Gorbunov
D. Kovalev
Dmitry Makarenko
Peter Richtárik
163
78
0
23 Oct 2020