Efficient Aggregation

Efficient aggregation techniques are crucial for distributed machine learning, enabling the combination of model updates from numerous decentralized sources without compromising privacy or efficiency. Current research focuses on developing robust aggregation methods that are resilient to data heterogeneity, outliers, and limited communication bandwidth, exploring weighted averaging, median-based approaches, and novel techniques like mirror space aggregation. These advancements are vital for improving the scalability and reliability of federated learning, decentralized systems, and other distributed applications across diverse fields, including biometrics and military applications.