# A Multivocal Literature Review on Privacy and Fairness in Federated Learning

arXiv: https://arxiv.org/abs/2408.08666 · HTML: https://mlsanity.com/paper/2408.08666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption](https://mlsanity.com/paper/2104.06824/) (2021)
- [GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning](https://mlsanity.com/paper/2105.00529/) (2021)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT](https://mlsanity.com/paper/2007.13585/) (2020)
- [Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System](https://mlsanity.com/paper/2111.06290/) (2021)
- [FAIR-FATE: Fair Federated Learning with Momentum](https://mlsanity.com/paper/2209.13678/) (2022)
- [Mitigating Group Bias in Federated Learning: Beyond Local Fairness](https://mlsanity.com/paper/2305.09931/) (2023)
- [Enabling Homomorphically Encrypted Inference for Large DNN Models](https://mlsanity.com/paper/2103.16139/) (2021)
- [Multi-dimensional Fair Federated Learning](https://mlsanity.com/paper/2312.05551/) (2023)
- [Fairness and Privacy in Federated Learning and Their Implications in Healthcare](https://mlsanity.com/paper/2308.07805/) (2023)
- [Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things](https://mlsanity.com/paper/2311.04944/) (2023)
- [Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification](https://mlsanity.com/paper/2310.19558/) (2023)
- [Dynamic Fair Federated Learning Based on Reinforcement Learning](https://mlsanity.com/paper/2311.00959/) (2023)

## Abstract (arXiv)

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.