Design of a Federated Learning System for IT Security: Towards Secure Human Resource Management
Lisa Verlande, Ulrike Lechner, Steffi Rudel · 2022
Federated learning is a new, decentralized type of machine learning in which the models, rather than the data, are shared to maintain privacy in machine learning. This paper aims to investigate the design of a federated learning system to increase IT security in Human Resource Management and, in particular, recruiting processes while complying with business needs and General Data Protection Regulation. We propose a federated learning system and a novel approach to identifying malware throughout the recruiting process. The combination of Design science, Service Science, and the reference modeling method guide our research design. This paper presents the results of the first design iterations with the identification of service design elements and the design of a recruiting process with a federated learning system inside.