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FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup
  for Non-IID Data

FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data

18 September 2023
Hao Sun
Li Shen
Shi-Yong Chen
Jingwei Sun
Jing Li
Guangzhong Sun
Dacheng Tao
    FedML
ArXivPDFHTML

Papers citing "FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data"

3 / 3 papers shown
Title
Patches Are All You Need?
Patches Are All You Need?
Asher Trockman
J. Zico Kolter
ViT
214
400
0
24 Jan 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
173
410
0
14 Jul 2021
Towards Practical Adam: Non-Convexity, Convergence Theory, and
  Mini-Batch Acceleration
Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration
Congliang Chen
Li Shen
Fangyu Zou
Wei Liu
36
26
0
14 Jan 2021
1