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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| Published in: | IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 11; p. 1 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
01.11.2022
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| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| Online Access: | Get full text |
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