Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluation of Hyperparameter-Optimization Approaches in an Industrial
  Federated Learning System   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.

5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys