Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent
While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals. As sensitive information may be contained in the individual's dataset, sharing training data may lead to severe privacy concerns... (read more)
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Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
SGD
|
Stochastic Optimization |