Optimal Aggregation

Optimal aggregation focuses on combining predictions or information from multiple sources to improve overall accuracy, robustness, and efficiency. Current research explores diverse aggregation strategies, including minimizing variance over minimizing error, min-bounded averaging for access control, and optimal transport methods for feature aggregation in applications like visual place recognition and federated learning. These advancements are crucial for enhancing the performance of machine learning models, improving the security and fairness of federated learning systems, and enabling more efficient distributed computation in various fields.