Joint Client Scheduling and Quantization Optimization in Energy Harvesting-Enabled Federated Learning Networks-Reference-Cited by-同舟云学术

Joint Client Scheduling and Quantization Optimization in Energy Harvesting-Enabled Federated Learning Networks

Author:

Ni Zhengwei¹^ORCID,Zhang Zhaoyang²^ORCID,Luong Nguyen Cong³^ORCID,Niyato Dusit⁴^ORCID,Kim Dong In⁵^ORCID,Feng Shaohan¹^ORCID

Affiliation:

1. School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Hangzhou, China

2. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

3. Faculty of Computer Science, Phenikaa University, Hanoi, Vietnam

4. School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore

5. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea

Funder

Zhejiang Provincial Natural Science Foundation of China

National Natural Science Foundation of China

National Key Research and Development Program of China

National Research Foundation, Singapore

Infocomm Media Development Authority under its Future Communications Research and Development Program

DSO National Laboratories under the AI Singapore Program

MOE Tier 1

National Research Foundation of Korea

Korean Government

Provincial Universities Basic Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

Reference64 articles.

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