A Triple-Step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement

Author:

You Linlin1,Liu Sheng1ORCID,Chang Yi2ORCID,Yuen Chau3ORCID

Affiliation:

1. School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China

2. School of Artificial Intelligence, Jilin University, Changchun, China

3. Engineering Product Development, Singapore University of Technology and Design, Tampines, Singapore

Funder

National Natural Science Foundation of China

Singapore Ministry of National Development and the National Research Foundation, Prime Minister’s Office through the Cities of Tomorrow (CoT) Research Programme

Collaborative Innovation Center for Transportation of Guangzhou

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Reference36 articles.

1. A mutual information based federated learning framework for edge computing networks

2. Efficient decentralized deep learning by dynamic model averaging;kamp;Proc Eur Conf Mach Learn Knowl Discovery Databases,2018

3. cpSGD: Communication-efficient and differentially-private distributed SGD;agarwal;Proc 32nd Int Conf Neural Inf Process Syst,2018

4. Jointly Optimizing Client Selection and Resource Management in Wireless Federated Learning for Internet of Things

5. How transferable are features in deep neural networks?;yosinski;Proc 27th Int Conf Neural Inf Process Syst,2014

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