# Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?

arXiv: https://arxiv.org/abs/2511.01737 · HTML: https://mlsanity.com/paper/2511.01737/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee](https://mlsanity.com/paper/2011.01783/) (2020)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning](https://mlsanity.com/paper/2103.13822/) (2021)
- [Fairness-Aware Client Selection for Federated Learning](https://mlsanity.com/paper/2307.10738/) (2023)
- [One-Shot Federated Learning with Classifier-Free Diffusion Models](https://mlsanity.com/paper/2502.08488/) (2025)
- [Federated Fairness Analytics: Quantifying Fairness in Federated Learning](https://mlsanity.com/paper/2408.08214/) (2024)
- [A Decentralized and Self-Adaptive Approach for Monitoring Volatile Edge Environments](https://mlsanity.com/paper/2405.07806/) (2024)
- [MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping](https://mlsanity.com/paper/2507.10158/) (2025)

## Abstract (arXiv)

Federated learning (FL) has emerged as a transformative paradigm for edge intelligence, enabling collaborative model training while preserving data privacy across distributed personal devices. However, the inherent volatility of edge environments, characterized by dynamic resource availability and heterogeneous client capabilities, poses significant challenges for achieving high accuracy and fairness in client participation. This paper investigates the fundamental trade-off between model accuracy and fairness in highly volatile edge environments. This paper provides an extensive empirical evaluation of fairness-based client selection algorithms such as RBFF and RBCSF against random and greedy client selection regarding fairness, model performance, and time, in three benchmarking datasets (CIFAR10, FashionMNIST, and EMNIST). This work aims to shed light on the fairness-performance and fairness-speed trade-offs in a volatile edge environment and explore potential future research opportunities to address existing pitfalls in textitfair client selection strategies in FL. Our results indicate that more equitable client selection algorithms, while providing a marginally better opportunity among clients, can result in slower global training in volatile environmentsfootnoteThe code for our experiments can be found at https://github.com/obaidullahzaland/FairFLFLTA.