FedComp: A Federated Learning Compression Framework for Resource-Constrained Edge Computing Devices-Reference-Cited by-同舟云学术

FedComp: A Federated Learning Compression Framework for Resource-Constrained Edge Computing Devices

Published:2024-01 Issue:1 Volume:43 Page:230-243
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Wu Donglei¹^ORCID,Yang Weihao¹,Jin Haoyu¹,Zou Xiangyu¹^ORCID,Xia Wen²,Fang Binxing³

Affiliation:

1. Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

2. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology, Shenzhen, China

3. Department of New Networks, Peng Cheng Laboratory, Shenzhen, China

Funder

Major Key Project of PCL

National Natural Science Foundation of China

Shenzhen Science and Technology Innovation Program

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

Reference52 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan

2. Federated optimization: Distributed machine learning for on-device intelligence;Konecný;arXiv:1610.02527,2016

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