Federated Learning is a decentralized approach to Machine Learning that preserves privacy by sharing models rather than data. This paper examines the requirements for a Federated Learning system as part of an IT service to strengthen IT security in Human Resource Management, especially in the recruitment process, while meeting the business needs of different stakeholders. Our research design is guided by design science research. This paper presents one iteration with a mixed-method approach consisting of a survey with n=110 data sets, a workshop, and ten expert interviews. The result shows up two requirements catalogs for service design and user experience.

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Track 4: Distributed Trust, Security & Privacy