Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
Journal: arXiv (Cornell University)
Year: 2024

In computer vision, the vision transformer (ViT) has increasingly superseded the convolutional neural network (CNN) for improved accuracy and robustness. However, ViT's large model sizes and high sample complexity make it difficult to train on resource-constrained edge devices. Split learning (SL) emerges as a viable solution, leveraging server-side resources to train ViTs while utilizing private data from distributed devices. However, SL requires additional information exchange for weight updat