This paper introduces an advanced approach for fortifying Federated Learning
(FL) systems against label-flipping attacks. We propose a simplified
consensus-based verification process integrated with an adaptive thresholding
mechanism. This dynamic thresholding is designed to adjust based on the
evolving landscape of model updates, offering a refined layer of anomaly
detection that aligns with the real-time needs of distributed learning
environments. Our method necessitates a majority consensus among participating
clients to validate updates, ensuring that only vetted and consensual
modifications are applied to the global model. The efficacy of our approach is
validated through experiments on two benchmark datasets in deep learning,
CIFAR-10 and MNIST. Our results indicate a significant mitigation of
label-flipping attacks, bolstering the FL system's resilience. This method
transcends conventional techniques that depend on anomaly detection or
statistical validation by incorporating a verification layer reminiscent of
blockchain's participatory validation without the associated cryptographic
overhead. The innovation of our approach rests in striking an optimal balance
between heightened security measures and the inherent limitations of FL
systems, such as computational efficiency and data privacy. Implementing a
consensus mechanism specifically tailored for FL environments paves the way for
more secure, robust, and trustworthy distributed machine learning applications,
where safeguarding data integrity and model robustness is critical.

提出了一种针对标签翻转攻击加固联邦学习系统的先进方法，该方法通过集成自适应阈值的简化一致性验证过程，提供了与分布式学习环境实时需求相适应的精炼的异常检测层，使参与客户必须达成多数共识以验证更新，确保只有经过审核和共识的修改应用于全局模型，验证实验证明了该方法显著减少了标签翻转攻击，增强了联邦学习系统的恢复能力，该方法通过结合类似区块链的参与式验证的验证层，超越了依赖异常检测或统计验证的传统技术，而无需产生与密码学相关的开销，我们的方法创新之处在于在加强安全措施与联邦学习系统的固有限制（如计算效率和数据隐私）之间取得最佳平衡，为更安全、强大和可信赖的分布式机器学习应用铺平了道路，其中数据完整性和模型鲁棒性的保护至关重要。