Aggregation Method

Aggregation methods in machine learning, particularly within federated learning, aim to combine locally trained models from multiple sources into a single, improved global model while preserving data privacy. Current research emphasizes developing robust aggregation techniques that address challenges like data heterogeneity, resource constraints, and adversarial attacks, often employing weighted averaging, gradient projection, and novel algorithms inspired by control theory or graph structures. These advancements are crucial for improving the accuracy, efficiency, and security of federated learning across diverse applications, including biomedical data analysis and resource-constrained environments.