# Tackling Selfish Clients in Federated Learning

arXiv: https://arxiv.org/abs/2407.15402 · HTML: https://mlsanity.com/paper/2407.15402/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients](https://mlsanity.com/paper/2207.09209/) (2022)
- [Provably Secure Federated Learning against Malicious Clients](https://mlsanity.com/paper/2102.01854/) (2021)
- [Free-rider Attacks on Model Aggregation in Federated Learning](https://mlsanity.com/paper/2006.11901/) (2020)
- [Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity](https://mlsanity.com/paper/2012.14453/) (2020)
- [Abnormal Client Behavior Detection in Federated Learning](https://mlsanity.com/paper/1910.09933/) (2019)
- [Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2012.06810/) (2020)
- [Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning](https://mlsanity.com/paper/2208.10273/) (2022)
- [Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks](https://mlsanity.com/paper/2212.11194/) (2022)

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

- [Can Quantum Federated Learning Withstand Circuit-Level Backdoors?](https://mlsanity.com/paper/2605.27416/) (2026)
- [Adaptive Bayesian Partner Selection for Federated Clinical Centers](https://mlsanity.com/paper/2609.16446/) (2026)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL is deployed into the wild, some intelligent clients can deliberately deviate from the standard training process to make the global model inclined toward their local model, thereby prioritizing their local data distribution. We refer to this novel category of misbehaving clients as selfish. In this paper, we propose a Robust aggregation strategy for FL server to mitigate the effect of Selfishness (in short RFL-Self). RFL-Self incorporates an innovative method to recover (or estimate) the true updates of selfish clients from the received ones, leveraging robust statistics (median of norms) of the updates at every round. By including the recovered updates in aggregation, our strategy offers strong robustness against selfishness. Our experimental results, obtained on MNIST and CIFAR-10 datasets, demonstrate that just 2% of clients behaving selfishly can decrease the accuracy by up to 36%, and RFL-Self can mitigate that effect without degrading the global model performance.