Personalized Federated Learning
Personalized Federated Learning is a federated learning paradigm that extends standard federated learning by enabling participant-specific model personalization to address heterogeneity in data distributions, thereby optimizing individual performance while leveraging shared global knowledge.
205
Publications
14.5K
Citations
813
Authors
282
Institutions
Publications per year
2017–2026
204
Variants
Pfl
Parents
205
1–5 of 205
813
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282

Sun Yat-sen University
Guangzhou, China
9 h-index
27 publications
3K citations
University of Technology Sydney
Sydney, Australia
7 h-index
20 publications
1.4K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
6 h-index
8 publications
1.3K citations

Shanghai Jiao Tong University
Shanghai, China
6 h-index
19 publications
2.8K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
5 h-index
17 publications
1.5K citations

Zhejiang University
Hangzhou, China
5 h-index
10 publications
593 citations

Huazhong University of Science and Technology
Wuhan, China
5 h-index
11 publications
848 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
16 publications
1.3K citations
1–8 of 282
Repository · 49 publications · 2.8K citations
24
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7 publications · 598 citations
7
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6 publications · 1.3K citations
6
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Conference · 6 publications · 1.4K citations
6
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5 publications · 310 citations
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1–5