A Multivocal Literature Review on Privacy and Fairness in Federated Learning

• URL: http://arxiv.org/abs/2408.08666v2
• Date: Sun, 27 Oct 2024 11:08:39 GMT
• Title: A Multivocal Literature Review on Privacy and Fairness in Federated Learning
• Authors: Beatrice Balbierer, Lukas Heinlein, Domenique Zipperling, Niklas Kühl,
• Abstract summary: Federated learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Recent research has demonstrated an inherent tension between privacy and fairness. We argue that the relationship between privacy and fairness has been neglected, posing a critical risk for real-world applications.
• Score: 1.6124402884077915
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 behaviour, 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.

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