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Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning
  without Labels

Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels

15 April 2024
Satyavrat Wagle
Seyyedali Hosseinalipour
Naji Khosravan
Christopher G. Brinton
    FedML
ArXivPDFHTML

Papers citing "Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels"

3 / 3 papers shown
Title
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
Zewei Long
Jiaqi Wang
Yaqing Wang
Houping Xiao
Fenglong Ma
FedML
32
22
0
09 Sep 2021
Device Sampling for Heterogeneous Federated Learning: Theory,
  Algorithms, and Implementation
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
Su Wang
Mengyuan Lee
Seyyedali Hosseinalipour
Roberto Morabito
M. Chiang
Christopher G. Brinton
FedML
62
108
0
04 Jan 2021
Adaptive Federated Learning in Resource Constrained Edge Computing
  Systems
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Shiqiang Wang
Tiffany Tuor
Theodoros Salonidis
K. Leung
C. Makaya
T. He
Kevin S. Chan
130
1,663
0
14 Apr 2018
1