Two Stage Aggregation

Two-stage aggregation is a machine learning technique that improves model training by first grouping similar data points or models (e.g., via clustering) and then performing a weighted aggregation of these groups. Current research focuses on applying this approach to diverse areas, including federated learning (where it addresses heterogeneity in client resources and data distributions), multi-task learning (improving efficiency and interpretability), and time series analysis (handling irregular data). This technique offers significant advantages in improving model accuracy, convergence speed, and resource efficiency across various applications, particularly in scenarios with large-scale, heterogeneous data.