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.