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Loop Improvement: An Efficient Approach for Extracting Shared Features
  from Heterogeneous Data without Central Server

Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Server

21 March 2024
Fei Li
C. K. Loo
W. S. Liew
Xiaofeng Liu
    FedML
ArXiv (abs)PDFHTMLGithub (1★)

Papers citing "Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Server"

16 / 16 papers shown
Title
FedALA: Adaptive Local Aggregation for Personalized Federated Learning
FedALA: Adaptive Local Aggregation for Personalized Federated Learning
Jianqing Zhang
Yang Hua
Hao Wang
Tao Song
Zhengui Xue
Ruhui Ma
Haibing Guan
FedML
70
229
0
02 Dec 2022
Where to Begin? On the Impact of Pre-Training and Initialization in
  Federated Learning
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen
Jianyu Wang
Kshitiz Malik
Maziar Sanjabi
Michael G. Rabbat
FedMLAI4CE
81
73
0
14 Oct 2022
A Multi-modal Fusion Framework Based on Multi-task Correlation Learning
  for Cancer Prognosis Prediction
A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction
Kaiwen Tan
Weixian Huang
Xiaofeng Liu
Jinlong Hu
Shoubin Dong
32
42
0
22 Jan 2022
FedLab: A Flexible Federated Learning Framework
FedLab: A Flexible Federated Learning Framework
Dun Zeng
Siqi Liang
Xiangjing Hu
Hui Wang
Zenglin Xu
FedML
66
114
0
24 Jul 2021
Rethinking Architecture Design for Tackling Data Heterogeneity in
  Federated Learning
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Liangqiong Qu
Yuyin Zhou
Paul Pu Liang
Yingda Xia
Feifei Wang
Ehsan Adeli
L. Fei-Fei
D. Rubin
FedMLAI4CE
67
183
0
10 Jun 2021
No Fear of Heterogeneity: Classifier Calibration for Federated Learning
  with Non-IID Data
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Mi Luo
Fei Chen
Dapeng Hu
Yifan Zhang
Jian Liang
Jiashi Feng
FedML
83
342
0
09 Jun 2021
CoAtNet: Marrying Convolution and Attention for All Data Sizes
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Zihang Dai
Hanxiao Liu
Quoc V. Le
Mingxing Tan
ViT
123
1,208
0
09 Jun 2021
Towards Personalized Federated Learning
Towards Personalized Federated Learning
A. Tan
Han Yu
Li-zhen Cui
Qiang Yang
FedMLAI4CE
327
873
0
01 Mar 2021
PFL-MoE: Personalized Federated Learning Based on Mixture of Experts
PFL-MoE: Personalized Federated Learning Based on Mixture of Experts
Binbin Guo
Yuan Mei
Danyang Xiao
Weigang Wu
Ye Yin
Hongli Chang
MoE
92
22
0
31 Dec 2020
Federated Continual Learning with Weighted Inter-client Transfer
Federated Continual Learning with Weighted Inter-client Transfer
Jaehong Yoon
Wonyoung Jeong
Giwoong Lee
Eunho Yang
Sung Ju Hwang
FedML
95
209
0
06 Mar 2020
Think Locally, Act Globally: Federated Learning with Local and Global
  Representations
Think Locally, Act Globally: Federated Learning with Local and Global Representations
Paul Pu Liang
Terrance Liu
Liu Ziyin
Nicholas B. Allen
Randy P. Auerbach
David Brent
Ruslan Salakhutdinov
Louis-Philippe Morency
FedML
117
563
0
06 Jan 2020
Federated Learning with Personalization Layers
Federated Learning with Personalization Layers
Manoj Ghuhan Arivazhagan
V. Aggarwal
Aaditya Kumar Singh
Sunav Choudhary
FedML
94
839
0
02 Dec 2019
A Downsampled Variant of ImageNet as an Alternative to the CIFAR
  datasets
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
P. Chrabaszcz
I. Loshchilov
Frank Hutter
SSegOOD
163
649
0
27 Jul 2017
A Survey on Multi-Task Learning
A Survey on Multi-Task Learning
Yu Zhang
Qiang Yang
AIMat
605
2,235
0
25 Jul 2017
Bag of Tricks for Efficient Text Classification
Bag of Tricks for Efficient Text Classification
Armand Joulin
Edouard Grave
Piotr Bojanowski
Tomas Mikolov
VLM
177
4,630
0
06 Jul 2016
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
406
17,559
0
17 Feb 2016
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