Requirements for a Federated Learning System to Strengthen IT Security in Human Resource Management

Lisa Kolb (), Steffi Rudel () and Ulrike Lechner ()
Additional contact information
Lisa Kolb: University of the Bundeswehr Munich
Steffi Rudel: University of the Bundeswehr Munich
Ulrike Lechner: University of the Bundeswehr Munich

A chapter in Solutions and Technologies for Responsible Digitalization, 2025, pp 3-22 from Springer

Abstract: Abstract 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.

Keywords: Federated learning; Human resource management; IT security; Requirements (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-80122-8_1

Ordering information: This item can be ordered from
http://www.springer.com/9783031801228

DOI: 10.1007/978-3-031-80122-8_1

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().