Hierarchical Federated Learning
Hierarchical Federated Learning is a federated learning framework that organizes participating devices into multiple layers of intermediary servers to enable localized model aggregation and improved scalability, communication efficiency, and heterogeneity management across distributed clients.
115
Publications
11.4K
Citations
486
Authors
211
Institutions
Publications per year
2017–2026
112
Variants
Hfl
Parents
115
Client-Edge-Cloud Hierarchical Federated Learning
Lumin Liu, Jun Zhang, Shenghui Song et al. · 2020 · 886 citations
1–5 of 115
486
6
h-index
4
h-index
Vellore Institute of Technology University, India
4 publications · 993 citations
4
h-index
4
h-index
4
h-index
1–5 of 486
211
Nanyang Technological University
Singapore, Singapore
7 h-index
16 publications
1.7K citations
Hamad bin Khalifa University
Doha, Qatar
6 h-index
9 publications
476 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
5 h-index
13 publications
3.4K citations
Mohamed bin Zayed University of Artificial Intelligence
Abu Dhabi, United Arab Emirates
5 h-index
8 publications
707 citations

Sun Yat-sen University
Guangzhou, China
5 h-index
17 publications
2.7K citations
Singapore University of Technology and Design
Singapore, Singapore
5 h-index
7 publications
668 citations

Qatar University
Doha, Qatar
5 h-index
20 publications
1.5K citations

University of Electronic Science and Technology of China
Chengdu, China
4 h-index
10 publications
2.1K citations
1–8 of 211
8 publications · 582 citations
8
h-index
TPDS · 7 publications · 721 citations
7
h-index
Repository · 7 publications · 276 citations
7
h-index
6 publications · 905 citations
6
h-index
4 publications · 541 citations
4
h-index
1–5