[2604.04611] Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns

Computer Science > Machine Learning arXiv:2604.04611 (cs) [Submitted on 6 Apr 2026] Title:Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns Authors:Motoki Nakamura View a PDF of the paper titled Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns, by Motoki Nakamura View PDF HTML (experimental) Abstract:Federated learning (FL) enables multiple clients to collaboratively train a global model by aggregating local updates without sharing private data. However, FL often faces 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 a leading candidate for practical free-rider detection methods, as it requires neither a proxy dataset nor pre-training. Nevertheless, it struggles to detect ``dynamic'' free-riders who behave honestly in early rounds and later switch to free-riding, particularly under global-model-mimicking attacks such as the delta weight attack and our newly proposed adaptive WEF-camouflage attack. In this paper, we propose a novel detection method S2-WEF that simulates the WEF patterns of potential global-model-based attacks on the server side using previously broadcasted global models, and identifies clients whose submitted WEF patterns resemble the simulated ones....