# Fairness and Privacy-Preserving in Federated Learning: A Survey

arXiv: https://arxiv.org/abs/2306.08402 · HTML: https://mlsanity.com/paper/2306.08402/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2023.

## Built on by (most cited first, 12 of 12)

- [Federated Fairness Analytics: Quantifying Fairness in Federated Learning](https://mlsanity.com/paper/2408.08214/) (2024)
- [Fairness in Federated Learning: Trends, Challenges, and Opportunities](https://mlsanity.com/paper/2509.00799/) (2025)
- [Swarm Learning: A Survey of Concepts, Applications, and Trends](https://mlsanity.com/paper/2405.00556/) (2024)
- [Estimation of conditional average treatment effects on distributed confidential data](https://mlsanity.com/paper/2402.02672/) (2024)
- [Privacy-preserving gradient-based fair federated learning](https://mlsanity.com/paper/2407.13881/) (2024)
- [Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering](https://mlsanity.com/paper/2407.19331/) (2024)
- [Fairness in Federated Learning: Fairness for Whom?](https://mlsanity.com/paper/2505.21584/) (2025)
- [SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning](https://mlsanity.com/paper/2604.26388/) (2026)
- [Data Quality in Edge Machine Learning: A State-of-the-Art Survey](https://mlsanity.com/paper/2406.02600/) (2024)
- [ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning](https://mlsanity.com/paper/2505.16850/) (2025)
- [Federated Learning at the Forefront of Fairness: A Multifaceted Perspective](https://mlsanity.com/paper/2602.00718/) (2026)
- [Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems](https://mlsanity.com/paper/2605.24458/) (2026)

## Abstract (arXiv)

Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting individualized training of decentralized edge clients on their own private data. The existing works, however, employ privacy mechanisms such as Secure Multiparty Computing (SMC), Differential Privacy (DP), etc. Which are immensely susceptible to interference, massive computational overhead, low accuracy, etc. With the increasingly broad deployment of FL systems, it is challenging to ensure fairness and maintain active client participation in FL systems. Very few works ensure reasonably satisfactory performances for the numerous diverse clients and fail to prevent potential bias against particular demographics in FL systems. The current efforts fail to strike a compromise between privacy, fairness, and model performance in FL systems and are vulnerable to a number of additional problems. In this paper, we provide a comprehensive survey stating the basic concepts of FL, the existing privacy challenges, techniques, and relevant works concerning privacy in FL. We also provide an extensive overview of the increasing fairness challenges, existing fairness notions, and the limited works that attempt both privacy and fairness in FL. By comprehensively describing the existing FL systems, we present the potential future directions pertaining to the challenges of privacy-preserving and fairness-aware FL systems.