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|a 10.1109/TPAMI.2024.3418862
|2 doi
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|a eng
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|a Huang, Wenke
|e verfasserin
|4 aut
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| 245 |
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|a Federated Learning for Generalization, Robustness, Fairness
|b A Survey and Benchmark
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| 264 |
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 08.11.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx of approaches have delivered towards different realistic challenges. In this survey, we provide a systematic overview of the important and recent developments of research on federated learning. First, we introduce the study history and terminology definition of this area. Then, we comprehensively review three basic lines of research: generalization, robustness, and fairness, by introducing their respective background concepts, task settings, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out several open issues in this field and suggest opportunities for further research
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|a Ye, Mang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shi, Zekun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wan, Guancheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, He
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Du, Bo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Qiang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
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|g 46(2024), 12 vom: 25. Dez., Seite 9387-9406
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|g day:25
|g month:12
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|u http://dx.doi.org/10.1109/TPAMI.2024.3418862
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