Federated Learning Theory
Federated Learning Theory is a theoretical framework within distributed machine learning that formally characterizes the principles, limitations, and guarantees of learning models across decentralized data sources while preserving data locality and privacy.
51
Publications
7.9K
Citations
208
Authors
87
Institutions
Publications per year
2017–2026
51
Parents
51
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent et al. · Foundations and Trends® in Machine Learning · 2020 · 4.7K citations · Full text
Federated Learning: Challenges, Methods, and Future Directions
IEEE Signal Processing Magazine · 2020 · 4.6K citations · Full text
1–5 of 51
208
Hong Kong University of Science and Technology, Hong Kong SAR China
3 publications · 269 citations
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2
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2
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2
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2
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1–5 of 208
87

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
5 h-index
7 publications
324 citations

University of Macau
Macao, Macao
3 h-index
4 publications
337 citations
Tsinghua University
Beijing, China
3 h-index
7 publications
319 citations
Tianjin University
Tianjin, China
2 h-index
3 publications
222 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
2 h-index
2 publications
93 citations

City University of Macau
Macao, Macao
2 h-index
2 publications
211 citations
Hainan University
Haikou, China
2 h-index
7 publications
833 citations
Huawei Technologies (China)
Shenzhen, China
2 h-index
5 publications
225 citations
1–8 of 87
Repository · 12 publications · 297 citations
11
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JISA · 2 publications · 165 citations
2
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2 publications · 77 citations
2
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TIST · 2 publications · 188 citations
2
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ComST · 1 publications · 33 citations
1
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1–5