  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.
