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DeceFL: A Principled Decentralized Federated Learning Framework

DeceFL: A Principled Decentralized Federated Learning Framework

15 July 2021
Ye Yuan
Jun Liu
Dou Jin
Zuogong Yue
Ruijuan Chen
Maolin Wang
Chuan Sun
Lei Xu
Feng Hua
Xincheng He
Xinlei Yi
Tao Yang
Hai-Tao Zhang
Shaochun Sui
Han Ding
    FedML
ArXivPDFHTML

Papers citing "DeceFL: A Principled Decentralized Federated Learning Framework"

3 / 3 papers shown
Title
Decentralized Federated Learning: Fundamentals, State of the Art,
  Frameworks, Trends, and Challenges
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
Enrique Tomás Martínez Beltrán
Mario Quiles Pérez
Pedro Miguel Sánchez Sánchez
Sergio López Bernal
Gérome Bovet
M. Pérez
Gregorio Martínez Pérez
Alberto Huertas Celdrán
FedML
29
222
0
15 Nov 2022
Threats to Federated Learning: A Survey
Threats to Federated Learning: A Survey
Lingjuan Lyu
Han Yu
Qiang Yang
FedML
202
434
0
04 Mar 2020
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
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