Adaptive Scheme

Adaptive schemes in machine learning aim to optimize model performance and resource utilization by dynamically adjusting parameters or algorithms based on data characteristics and system conditions. Current research focuses on developing self-adaptive algorithms, such as variations of ADMM and gradient-based methods, to improve efficiency and robustness in federated learning, streaming data processing, and other applications. These advancements address challenges like data heterogeneity, limited resources, and the need for minimal computational overhead, ultimately leading to more efficient and accurate models across diverse domains.