A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy
As deep learning models are usually massive and complex, distributed learning is essential for increasing training efficiency. Moreover, in many real-world application scenarios like healthcare, distributed learning can also keep the data local and protect privacy. Recently, the asynchronous decentr...
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| Vydané v: | IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 11; s. 1 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
01.11.2022
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| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| On-line prístup: | Získať plný text |
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