Q Aggregation

Q-aggregation encompasses a range of techniques for combining multiple data sources or models to improve prediction accuracy or decision-making, addressing challenges in areas like federated learning and causal inference. Current research focuses on developing robust aggregation methods, particularly those employing reinforcement learning or information-theoretic approaches, to mitigate adversarial attacks and handle diverse data distributions. These advancements are significant for improving the reliability and efficiency of machine learning systems across various applications, including personalized medicine and multi-objective AI.