A Multivocal Literature Review on Privacy and Fairness in Federated Learning

Beatrice Balbierer, Lukas Heinlein, Domenique Zipperling () and Niklas Kühl
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Beatrice Balbierer: University of Bayreuth
Lukas Heinlein: University of Bayreuth
Domenique Zipperling: University of Bayreuth
Niklas Kühl: University of Bayreuth

A chapter in Artificial Intelligence, Data, and Decision-Making, 2026, pp 275-291 from Springer

Abstract: Abstract Federated Learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Yet, research has shown that information can still be extracted during training, making additional privacy-preserving measures such as differential privacy imperative. To implement real-world federated learning applications, fairness, ranging from a fair distribution of performance to non-discriminative behavior, must be considered. Particularly in high-risk applications (e.g. healthcare), avoiding the repetition of past discriminatory errors is paramount. As recent research has demonstrated an inherent tension between privacy and fairness, we conduct a multivocal literature review to examine the current methods to integrate privacy and fairness in federated learning. Our analyses illustrate that the relationship between privacy and fairness has been neglected, posing a critical risk for real-world applications. We highlight the need to explore the relationship between privacy, fairness, and performance, advocating for the creation of integrated federated learning frameworks.

Keywords: Federated Learning; Machine Learning; Fairness; Privacy (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-08480-4_18

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