# Learning Rate Adaptation for Federated and Differentially Private Learning

arXiv: https://arxiv.org/abs/1809.03832 · HTML: https://mlsanity.com/paper/1809.03832/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing](https://mlsanity.com/paper/2106.04502/) (2021)
- [FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning](https://mlsanity.com/paper/2112.08524/) (2021)
- [pfl-research: simulation framework for accelerating research in Private Federated Learning](https://mlsanity.com/paper/2404.06430/) (2024)
- [On Noisy Evaluation in Federated Hyperparameter Tuning](https://mlsanity.com/paper/2212.08930/) (2022)
- [Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis](https://mlsanity.com/paper/2202.08338/) (2022)
- [FEATHERS: Federated Architecture and Hyperparameter Search](https://mlsanity.com/paper/2206.12342/) (2022)
- [FedPop: Federated Population-based Hyperparameter Tuning](https://mlsanity.com/paper/2308.08634/) (2023)

## Abstract (arXiv)

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. The algorithm is applied in two separate frameworks: federated and differentially private learning. Using examples of deep neural networks we empirically show that the adaptive algorithm is competitive with manually tuned commonly used optimisation methods for differentially privately training. We also show that it works robustly in the case of federated learning unlike commonly used optimisation methods.