Dynamic Free-Rider Detection in Cross-Silo Federated Learning via Simulated Attack Patterns
Federated learning (FL) enables multiple clients to collaboratively train a global model by aggregating local updates without sharing private data. In this work, we focus on cross-silo FL, where each client typically represents an independent organization. However, cross-silo FL can face the challenge of free-riders, clients who submit fake model parameters without performing actual training to obtain the global model without contributing. Chen et al. proposed a free-rider detection method based on the weight evolving frequency (WEF) of model parameters. This detection approach is practical because it requires neither a proxy dataset nor pre-training. Nevertheless, it struggles to detect ...
| Authors | Motoki Nakamura |
|---|---|
| Primary category | cs.LG |
| Published | 6 April 2026 |
| Last updated at source | 8 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.