Incremental Aggregation

Incremental aggregation (IA) is a technique in distributed machine learning that improves efficiency by aggregating model updates at intermediate nodes in a network, rather than solely at a central server. Current research focuses on optimizing IA for various settings, including federated learning with sparse updates and meta-reinforcement learning, exploring novel aggregation functions beyond simple averaging (e.g., weighted power means) and dynamically adjusting parameters like mini-batch size and aggregation frequency to balance convergence speed, resource consumption, and model accuracy. These advancements are significant for improving the scalability and efficiency of distributed machine learning across diverse applications, such as federated learning in resource-constrained environments and real-time reinforcement learning.