Consistent Aggregation

Consistent aggregation focuses on combining information from multiple sources to produce a reliable and accurate overall result, addressing challenges arising from data heterogeneity, limited communication, and diverse objectives. Current research explores various aggregation methods, including weighted averaging, novel architectures like mirror space aggregation and deep ensemble learning, and the application of differential privacy for sensitive data. This field is crucial for advancing distributed machine learning, improving the efficiency of large-scale data analysis, and ensuring fairness and privacy in collaborative learning environments across diverse applications like federated learning and environmental monitoring.