Validation Dataset

Validation datasets are crucial in machine learning for evaluating model performance and generalization ability, but acquiring representative validation data can be challenging, particularly in federated learning. Current research focuses on developing validation-free methods, often employing techniques like cross-round valuation or meta-learning to optimize the selection of validation samples, including the use of "hard" samples to improve generalization. These advancements are significant because they improve model reliability and efficiency, impacting various applications from medical error detection to image classification and breast cancer diagnosis, where access to large, representative validation sets may be limited.