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.

翻译：联邦学习使多个客户端能够在不共享私有数据的情况下，通过聚合本地更新协作训练全局模型。然而，联邦学习常面临搭便车者的挑战，即那些提交虚假模型参数而不执行实际训练、并未作贡献却获取全局模型的客户端。Chen等人提出了一种基于模型参数权重演化频率的搭便车检测方法。该检测方法无需代理数据集或预训练，是实用搭便车检测方法中的领先候选方案。然而，该方法难以检测"动态"搭便车者——这些客户端在早期轮次诚实行为，后期转而搭便车，特别是在面对全局模型模仿攻击（如delta权重攻击及其新提出的自适应权重演化频率伪装攻击）时。本文提出一种新型检测方法S2-WEF，该方法在服务器端利用先前广播的全局模型模拟潜在全局模型攻击的权重演化频率模式，并识别提交的权重演化频率模式与模拟模式相似的客户端。为应对多种搭便车攻击策略，S2-WEF进一步将该基于模拟的相似度分数与通过提交权重演化频率相互比较计算的偏差分数相结合，通过二维聚类和逐分数分类将良性客户端与搭便车客户端分离。该方法无需代理数据集或预训练，即可动态检测训练过程中转变为搭便车者的客户端。我们在三个数据集和五种攻击类型上进行了广泛实验，表明S2-WEF比现有方法具有更高的鲁棒性。