Federated Optimization
Federated Optimization is an optimization framework in machine learning that coordinates model parameter updates across decentralized devices with local data, preserving data locality while minimizing a global objective through collaborative training.
287
Publications
24.3K
Citations
883
Authors
323
Institutions
Publications per year
2017–2026
283
287
Federated Learning: Challenges, Methods, and Future Directions
IEEE Signal Processing Magazine · 2020 · 4.6K citations · Full text
1–5 of 287
883
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King Abdullah University of Science and Technology, Saudi Arabia
5 publications · 285 citations
5
h-index
1–5 of 883
323
Carnegie Mellon University
Pittsburgh, United States
9 h-index
22 publications
20.4K citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
8 h-index
17 publications
675 citations
Nanyang Technological University
Singapore, Singapore
8 h-index
10 publications
247 citations

University of Macau
Macao, Macao
7 h-index
15 publications
726 citations
Google (United States)
Mountain View, United States
6 h-index
11 publications
1.8K citations

King Abdullah University of Science and Technology
Jeddah, Saudi Arabia
6 h-index
12 publications
526 citations

Southeast University
Nanjing, China
6 h-index
14 publications
348 citations

City University of Macau
Macao, Macao
6 h-index
14 publications
682 citations
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Repository · 62 publications · 7.6K citations
28
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9 publications · 604 citations
12
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8 publications · 489 citations
9
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7 publications · 718 citations
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Conference · 4 publications · 921 citations
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