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Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized
  Floating Aggregation Point

Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point

26 March 2022
Bhargav Ganguly
Seyyedali Hosseinalipour
Kwang Taik Kim
Christopher G. Brinton
Vaneet Aggarwal
David J. Love
M. Chiang
    FedML
ArXivPDFHTML

Papers citing "Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point"

3 / 3 papers shown
Title
Latency Optimization for Blockchain-Empowered Federated Learning in
  Multi-Server Edge Computing
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing
Dinh C. Nguyen
Seyyedali Hosseinalipour
David J. Love
P. Pathirana
Christopher G. Brinton
26
47
0
18 Mar 2022
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
1,687
0
14 Apr 2018
1