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FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning
  Communications

FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications

20 December 2023
Grant Wilkins
Sheng Di
Jon C. Calhoun
Zilinghan Li
Kibaek Kim
Robert Underwood
Richard Mortier
Franck Cappello
    FedML
ArXivPDFHTML

Papers citing "FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications"

4 / 4 papers shown
Title
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
Zilinghan Li
Shilan He
Ze Yang
Minseok Ryu
Kibaek Kim
Ravi K. Madduri
FedML
48
5
0
17 Sep 2024
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation
Shourya Bose
Yu Zhang
Kibaek Kim
11
3
0
21 Nov 2023
Federated Learning Challenges and Opportunities: An Outlook
Federated Learning Challenges and Opportunities: An Outlook
Jie Ding
Eric W. Tramel
Anit Kumar Sahu
Shuang Wu
Salman Avestimehr
Tao Zhang
FedML
26
53
0
01 Feb 2022
Universal Deep Neural Network Compression
Universal Deep Neural Network Compression
Yoojin Choi
Mostafa El-Khamy
Jungwon Lee
MQ
81
84
0
07 Feb 2018
1