SPARK: Efficient Decentralized Learning Through Stage-wise Projected NTK and Accelerated Regularization

The paper presents SPARK, a novel approach for communication-efficient decentralized learning. It leverages stage-wise projected Neural Tangent Kernel (NTK) and accelerated regularization techniques to improve performance in decentralized settings, a significant contribution to distributed AI research.