Introduces modulated learning for private distributed regression allowing one sample per client via calibrated noise injection on samples and aggregation of transformed representations to achieve unbiased gradients in expectation.
arXiv preprint arXiv:2012.03837 , year=
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Modulated learning for private and distributed regression with just a single sample per client device
Introduces modulated learning for private distributed regression allowing one sample per client via calibrated noise injection on samples and aggregation of transformed representations to achieve unbiased gradients in expectation.