Mean Aggregation

Mean aggregation, the process of combining multiple data points or model predictions into a single representative value, is a fundamental task across numerous scientific fields. Current research focuses on developing robust and efficient aggregation methods that address challenges like data heterogeneity (e.g., in federated learning), model instability (e.g., in ensemble methods), and adversarial attacks. These advancements are improving the accuracy, reliability, and security of machine learning models and enabling more sophisticated analyses in diverse applications, from image processing and financial forecasting to truth discovery and scientific benchmarking.