# Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency Source: [Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:15:31.717Z Authors: Jinyin Chen, Mingjun Li, Tao Liu, Haibin Zheng, Yao Cheng, Changting Lin Published: 2022-06-11T03:11:13.000Z ## Abstract 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. ## 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 IID Scenario 1](): Free-rider Detection; MNIST IID Scenario 1; F1 Score: 99. - [Free-rider Detection on CIFAR-10 Non-IID Scenario 1](): Free-rider Detection; CIFAR-10 Non-IID Scenario 1; F1 Score: 16. ## Original sources and implementation [Original paper]() [PDF]()