Label Flipping Attack

Label flipping attacks target machine learning models by corrupting training data labels, causing misclassification and model degradation. Current research focuses on developing robust defenses, employing techniques like consensus-based model validation, Bayesian aggregation, and anomaly detection using gradient analysis or performance weighting to identify and mitigate the impact of poisoned data. These efforts are crucial for enhancing the security and reliability of machine learning systems across various applications, particularly in federated learning and sensitive domains like cybersecurity and healthcare where data integrity is paramount.