Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System
Abstract
Federated Learning (FL) decouples model training from the need for direct access to the data and allows organizations to collaborate with industry partners to reach a satisfying level of performance without sharing vulnerable business information. The performance of a machine learning algorithm is highly sensitive to the choice of its hyperparameters. In an FL setting, hyperparameter optimization poses new challenges. In this work, we investigated the impact of different hyperparameter optimization approaches in an FL system. In an effort to reduce communication costs, a critical bottleneck in FL, we investigated a local hyperparameter optimization approach that -- in contrast to a global hyperparameter optimization approach -- allows every client to have its own hyperparameter configuration. We implemented these approaches based on grid search and Bayesian optimization and evaluated the algorithms on the MNIST data set using an i.i.d. partition and on an Internet of Things (IoT) sensor based industrial data set using a non-i.i.d. partition.
Cite
@article{arxiv.2110.08202,
title = {Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System},
author = {Stephanie Holly and Thomas Hiessl and Safoura Rezapour Lakani and Daniel Schall and Clemens Heitzinger and Jana Kemnitz},
journal= {arXiv preprint arXiv:2110.08202},
year = {2021}
}
Comments
This paper is accepted at the IDSC https://idsc.at/ and will be published by Springer. The Version uploaded is before the peer review process. The link to the final version will be updated as soon as the paper is published. Figure one was corrected on 2021/10/20