# Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency

arXiv: https://arxiv.org/abs/2206.05406 · HTML: https://mlsanity.com/paper/2206.05406/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 11 Jun 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Free-rider Attacks on Model Aggregation in Federated Learning](https://mlsanity.com/paper/2006.11901/) (2020)
- [Free-riders in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/1911.12560/) (2019)
- [A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning](https://mlsanity.com/paper/2011.10464/) (2020)
- [Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2012.06810/) (2020)
- [Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition](https://mlsanity.com/paper/1706.06720/) (2017)
- [How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning](https://mlsanity.com/paper/2112.01039/) (2021)
- [Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorch](https://mlsanity.com/paper/2202.09888/) (2022)
- [Attacks and Defenses for Free-Riders in Multi-Discriminator GAN](https://mlsanity.com/paper/2201.09967/) (2022)

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

- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [FRIDA: Free-Rider Detection using Privacy Attacks](https://mlsanity.com/paper/2410.05020/) (2024)
- [Dynamic Free-Rider Detection in Cross-Silo Federated Learning via Simulated Attack Patterns](https://mlsanity.com/paper/2604.04611/) (2026)

## Abstract (arXiv)

Federated learning (FL) is a distributed machine learning approach where multiple clients collaboratively train a joint model without exchanging their data. Despite FL's unprecedented success in data privacy-preserving, its vulnerability to free-rider attacks has attracted increasing attention. Existing defenses may be ineffective against highly camouflaged or high percentages of free riders. To address these challenges, we reconsider the defense from a novel perspective, i.e., model weight evolving frequency.Empirically, we gain a novel insight that during the FL's training, the model weight evolving frequency of free-riders and that of benign clients are significantly different. Inspired by this insight, we propose a novel defense method based on the model Weight Evolving Frequency, referred to as WEF-Defense.Specifically, we first collect the weight evolving frequency (defined as WEF-Matrix) during local training. For each client, it uploads the local model's WEF-Matrix to the server together with its model weight for each iteration. The server then separates free-riders from benign clients based on the difference in the WEF-Matrix. Finally, the server uses a personalized approach to provide different global models for corresponding clients. Comprehensive experiments conducted on five datasets and five models demonstrate that WEF-Defense achieves better defense effectiveness than the state-of-the-art baselines.