# Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns Source: [Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns]() Collection index: [llms.txt]() Data updated: 2026-06-16T05:00:05.515Z Authors: Motoki Nakamura Published: 2026-04-06T11:54:05.000Z ## 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. To handle a variety of free-rider attack strategies, S2-WEF further combines this simulation-based similarity score with a deviation score computed from mutual comparisons among submitted WEFs, and separates benign and free-rider clients by two-dimensional clustering and per-score classification. This method enables dynamic detection of clients that transition into free-riders during training without proxy datasets or pre-training. We conduct extensive experiments across three datasets and five attack types, demonstrating that S2-WEF achieves higher robustness than existing approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Free-rider Detection on MNIST Non-IID Scenario 2](): Free-rider Detection; MNIST Non-IID Scenario 2; F1 Score: 100. - [Free-rider Detection on CIFAR-10 Non-IID Scenario 1](): Free-rider Detection; CIFAR-10 Non-IID Scenario 1; F1 Score: 99. - [Free-rider Detection on MNIST IID Scenario 1](): Free-rider Detection; MNIST IID Scenario 1; F1 Score: 99. ## Original sources and implementation [Original paper]() [PDF]()