Numerical Aggregation

Numerical aggregation in machine learning focuses on efficiently and robustly combining data or model updates from multiple sources, addressing challenges like data heterogeneity and communication overhead. Current research emphasizes developing novel aggregation algorithms, such as those based on superquantiles for robust performance or analytic solutions for faster convergence in federated learning, and optimizing architectures like transformers for efficient multi-level feature aggregation in tasks such as semantic segmentation and question answering. These advancements improve the scalability and accuracy of machine learning models across diverse applications, including federated learning, natural language processing, and multi-criteria decision analysis, while also enhancing privacy and explainability.