Server Side Aggregation

Server-side aggregation in distributed machine learning focuses on efficiently combining model updates from multiple sources (e.g., devices or clients) to train a shared model. Current research emphasizes developing robust aggregation methods that address challenges like data heterogeneity (non-IID data) and adversarial attacks, often employing techniques such as weighted averaging, gradient projection, and dynamic distillation to improve model convergence and accuracy. These advancements are crucial for enabling practical federated learning applications, particularly in scenarios with limited communication bandwidth or unreliable data sources, and are improving the performance and resilience of distributed machine learning systems.