Global Sampling

Global sampling techniques aim to efficiently and effectively aggregate data from diverse, distributed sources for improved model training and decision-making. Current research focuses on optimizing sampling strategies within various contexts, including federated learning (e.g., using credit assignment to select informative clients) and distributed deep learning (e.g., employing novel sampling methods like uniform global sampling and Latent Dirichlet Sampling to address challenges like straggler effects and non-IID data). These advancements are crucial for enhancing the performance and scalability of machine learning models in resource-constrained environments and improving the efficiency of algorithms in areas such as path planning and robotics.