A Multivocal Literature Review on Privacy and Fairness in Federated Learning
<|reference_start|>A Multivocal Literature Review on Privacy and Fairness in Federated Learning: 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.<|reference_end|>
@article{balbierer2024a,
    title={A Multivocal Literature Review on Privacy and Fairness in Federated
  Learning},
    author={Beatrice Balbierer, Lukas Heinlein, Domenique Zipperling, Niklas
  K"uhl},
    journal={arXiv preprint arXiv:2408.08666},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08666},
    primaryClass={cs.LG cs.AI}
}