2 research outputs found
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications
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
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On the Role of Spatial Data Science for Federated Learning
Federated learning (FL) has the potential to mitigate privacy risks and communication costs associated with classical machine learning and data science approaches. Given the distributed nature of FL, many of its use cases face challenges related to spatiotemporal data, geographical analysis, and spatial statistics. However, so far, FL has received little attention by the GIScience community. In this paper, we provide a first overview of the key challenges in FL and how they relate to spatial data science. This paper thus aims to provide the basis for future contributions to federated learning practices by the (geo)spatial research community