FedCNO

Smart contract vulnerability detection is a critical task in ensuring the security of blockchain systems. However, challenges such as non-public source code and label noise, especially systematic noise, significantly hinder the effectiveness of detection models.  This paper introduces FedCNO (Federated Contract Noise Optimizer), an innovative federated learning framework designed to address these issues. FedCNO uniquely integrates local and global label correction mechanisms to improve label accuracy while maintaining data privacy. Additionally, it introduces a consistency score method to dynamically adjust loss weights based on sample reliability, ensuring robust learning even in high-noise environments. Through these mechanisms, FedCNO effectively mitigates the impact of both random and systematic noise, demonstrating superior robustness and adaptability compared to existing approaches. Under 30\% random noise, FedCNO achieved an F1 score of 87.86\% on the CBGRU model. Under 30\% systematic noise, it achieved an F1 score of 72.59\%. In contrast, other baseline methods failed to improve the performance of detection models under systematic noise and even caused significant performance degradation. Experimental results demonstrate that FedCNO exhibits exceptional robustness in handling both random and systematic label noise, significantly outperforming state-of-the-art federated learning approaches.  Beyond noise correction, the framework promotes collaborative model optimization across distributed datasets without compromising sensitive information. Future work will further explore its applicability to diverse types of vulnerabilities and other blockchain security challenges.