ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2208.02856
  4. Cited By
Embedding Alignment for Unsupervised Federated Learning via Smart Data
  Exchange

Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange

4 August 2022
Satyavrat Wagle
Seyyedali Hosseinalipour
Naji Khosravan
M. Chiang
Christopher G. Brinton
    FedML
ArXivPDFHTML

Papers citing "Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange"

7 / 7 papers shown
Title
Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning
  without Labels
Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels
Satyavrat Wagle
Seyyedali Hosseinalipour
Naji Khosravan
Christopher G. Brinton
FedML
32
2
0
15 Apr 2024
Smart Information Exchange for Unsupervised Federated Learning via
  Reinforcement Learning
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
Seohyun Lee
Anindya Bijoy Das
Satyavrat Wagle
Christopher G. Brinton
FedML
17
0
0
15 Feb 2024
Device Sampling and Resource Optimization for Federated Learning in
  Cooperative Edge Networks
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Su Wang
Roberto Morabito
Seyyedali Hosseinalipour
Mung Chiang
Christopher G. Brinton
FedML
24
7
0
07 Nov 2023
Decentralized Federated Learning: A Survey and Perspective
Decentralized Federated Learning: A Survey and Perspective
Liangqi Yuan
Ziran Wang
Lichao Sun
Philip S. Yu
Christopher G. Brinton
FedML
34
85
0
02 Jun 2023
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
A. Das
A. Ramamoorthy
David J. Love
Christopher G. Brinton
FedML
42
4
0
23 Feb 2023
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
77
110
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
144
1,685
0
14 Apr 2018
1