  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.
