
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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