摘要： 联邦学习通过“数据不动模型动”的分布式范式实现了隐私保护与协同建模，但现有方案在客户端选择效率、恶意节点防御及激励分配公平性上存在明显不足。为此，本研究提出一种动态恶意节点识别机制(GIFL)，实现恶意节点精准识别、高效客户端选择与动态激励分配的协同优化。GIFL通过轻量型贪心筛选策略过滤低贡献高成本节点，基于模型参数偏差的影响因子动态更新机制识别并剔除恶意节点，结合历史与实时贡献设计动态报酬支付策略。基于Fashion-MNIST和CIFAR-10及 Tiny-ImageNet 数据集的实验表明，在恶意节点比例为5%-30%的跨设备联邦学习场景下，与FedAvg、IAFL等五种基准方法相比，GIFL的恶意节点识别精度提升5.4%~23.9%，前置筛选耗时较QAIM平均降低86.1%，模型收敛稳定性与社会福利显著改善，在模型精度不低于92%（Fashion-MNIST、CIFAR-10）和88%（Tiny-ImageNet）

Abstract: Federated learning achieves privacy preservation and collaborative modeling through the distributed paradigm of “data staying local and model being shared.” However, existing schemes show clear limitations in client selection efficiency, malicious node defense, and fairness of incentive allocation. This paper proposes a dynamic malicious node identification mechanism, named GIFL, to jointly optimize malicious node detection, efficient client selection, and dynamic incentive allocation. GIFL adopts a lightweight greedy screening strategy to filter low-contribution and high-cost clients. An influence factor dynamic updating mechanism based on model parameter deviation is used to accurately identify and remove malicious nodes. A dynamic reward payment strategy is designed by jointly considering historical and real-time contributions. Experiments on the Fashion-MNIST, CIFAR-10 and Tiny-ImageNet datasets demonstrate that in cross-device federated learning scenarios where the proportion of malicious nodes is 5%-30%, GIFL significantly outperforms five benchmark methods, including FedAvg and IAFL. The malicious node identification accuracy is improved by 5.4% to 23.9%. Compared with QAIM, the pre-selection time is reduced by an average of 86.1%. Model convergence stability and social welfare are significantly enhanced. Under the condition that model accuracy is not lower than 92% (Fashion-MNIST, CIFAR-10) and 88% (Tiny-ImageNet), the average server cost is reduced by 16.94%. The results indicate that GIFL provides an effective and reliable solution for federated learning in mobile edge networks.