Aggregation Function

Aggregation functions combine multiple data points into a single representative value, a crucial step in many machine learning and data analysis tasks. Current research focuses on developing robust and efficient aggregation methods for diverse applications, including federated learning (where security and fairness are paramount), graph neural networks (improving expressivity and mitigating over-smoothing), and few-shot learning (enhancing model accuracy with limited data). These advancements are improving the performance and reliability of machine learning models across various domains, from medical diagnosis to materials science, by addressing challenges like data poisoning, free-riding, and the need for more informative summaries of complex models.