Mask-Encoded Sparsification: A Communication-Efficient Approach for Split Learning to Mitigate Biased Gradients

Mask-encoded sparsification (MS) is a novel framework that significantly reduces communication overhead in Split Learning (SL) scenarios without compromising model convergence or generalization capabilities.