Decentralized Kernel Ridge Regression

Decentralized Kernel Ridge Regression (DKRR) focuses on efficiently performing kernel ridge regression across distributed datasets, addressing challenges like data silos and communication limitations. Current research emphasizes developing robust and adaptive algorithms, such as those employing the Lepskii principle or data-dependent random features, to improve accuracy and convergence while minimizing communication overhead. These advancements are crucial for handling large-scale datasets in various applications, including IoT data analysis and federated learning, where data privacy and computational efficiency are paramount. The development of communication-efficient and robust DKRR methods is driving progress in distributed machine learning.