Learning Rate Adaptation for Federated and Differentially Private Learning
We propose an algorithm for the adaptation of the learning rate for stochastic gradient descent (SGD) that avoids the need for validation set use. The idea for the adaptiveness comes from the technique of extrapolation: to get an estimate for the error against the gradient flow which underlies SGD, we compare the result obtained by one full step and two half-steps... (read more)
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Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
SGD
|
Stochastic Optimization |