Lasse Wrobel works as a research assistant for the BEI. He completed his bachelor's degree at the University of Leipzig in business informatics with a research focus on disruptive technologies. Since then, he has been investigating the influence of artificial intelligence and blockchain on companies in various industries. Currently, Lasse is studying for a Master's degree in Business Informatics.

Federated Learning – Efficient Machine Learning That Respects Privacy?

In the financial industry, customers expect high standards with regard to data protection and the integrity of their own data. Nevertheless, from the perspective of value creation, it is essential for banks to evaluate customer data using statistical methods and algorithms. Banks are thus caught in a conflict between maintaining data privacy and enforcing their own business model. To address this problem, the concept of “federated learning” has become established on the market in recent years, in which the data used for model training is always stored decentrally and the models are trained decentrally.

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“Data-centric AI” – A shift in the AI mindset?

The traditional approach to AI focuses on the process of training the model. The underlying data is often a secondary concern. This approach works particularly well for Internet corporations, as they have vast amounts of data and the capabilities to analyze it. In contrast, there is little potential for using AI in small businesses with this approach due to a lack of data. Therefore, it is worth taking a look at the data – moving away from model-centric to data-centric AI.

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