Data-driven machine learning is playing a crucial role in the advancements of
Industry 4.0, specifically in enhancing predictive maintenance and quality
inspection. Federated learning (FL) enables multiple participants to develop a
machine learning model without compromising the privacy and confidentiality of
their data. In this paper, we evaluate the performance of different FL
aggregation methods and compare them to central and local training approaches.
Our study is based on four datasets with varying data distributions. The
results indicate that the performance of FL is highly dependent on the data and
its distribution among clients. In some scenarios, FL can be an effective
alternative to traditional central or local training methods. Additionally, we
introduce a new federated learning dataset from a real-world quality inspection
setting