# Fairness and Privacy in Federated Learning and Their Implications in Healthcare

arXiv: https://arxiv.org/abs/2308.07805 · HTML: https://mlsanity.com/paper/2308.07805/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning](https://mlsanity.com/paper/2011.10464/) (2020)
- [Fairness and Accuracy in Federated Learning](https://mlsanity.com/paper/2012.10069/) (2020)
- [FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning](https://mlsanity.com/paper/2106.02310/) (2021)
- [Fair Federated Learning via Bounded Group Loss](https://mlsanity.com/paper/2203.10190/) (2022)
- [Intersectionality: Multiple Group Fairness in Expectation Constraints](https://mlsanity.com/paper/1811.09960/) (2018)
- [Fairness in Federated Learning for Spatial-Temporal Applications](https://mlsanity.com/paper/2201.06598/) (2022)

## Built on by (most cited first, 3 of 3)

- [Fairness in Federated Learning: Fairness for Whom?](https://mlsanity.com/paper/2505.21584/) (2025)
- [Horizontal Federated Computer Vision](https://mlsanity.com/paper/2401.00390/) (2023)
- [A Multivocal Literature Review on Privacy and Fairness in Federated Learning](https://mlsanity.com/paper/2408.08666/) (2024)

## Abstract (arXiv)

Currently, many contexts exist where distributed learning is difficult or otherwise constrained by security and communication limitations. One common domain where this is a consideration is in Healthcare where data is often governed by data-use-ordinances like HIPAA. On the other hand, larger sample sizes and shared data models are necessary to allow models to better generalize on account of the potential for more variability and balancing underrepresented classes. Federated learning is a type of distributed learning model that allows data to be trained in a decentralized manner. This, in turn, addresses data security, privacy, and vulnerability considerations as data itself is not shared across a given learning network nodes. Three main challenges to federated learning include node data is not independent and identically distributed (iid), clients requiring high levels of communication overhead between peers, and there is the heterogeneity of different clients within a network with respect to dataset bias and size. As the field has grown, the notion of fairness in federated learning has also been introduced through novel implementations. Fairness approaches differ from the standard form of federated learning and also have distinct challenges and considerations for the healthcare domain. This paper endeavors to outline the typical lifecycle of fair federated learning in research as well as provide an updated taxonomy to account for the current state of fairness in implementations. Lastly, this paper provides added insight into the implications and challenges of implementing and supporting fairness in federated learning in the healthcare domain.