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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
ArXivPDFHTML

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

3 / 3 papers shown
Title
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
19
42
0
22 Jan 2022
Towards Personalized Federated Learning
Towards Personalized Federated Learning
A. Tan
Han Yu
Li-zhen Cui
Qiang Yang
FedML
AI4CE
209
840
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
38
22
0
31 Dec 2020
1