DegreeSpar: Structured Degree Sparsity for Efficient Secure Transformer Inference

arXiv:2609.32204 · cs.CR, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CR, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Accuracy — 62× in this paper · explained in 1 episode
• Transformer — 51× in this paper · explained in 2 episodes
• Sparsity — 48× in this paper · explained in 4 episodes
• Pruning — 33× in this paper · explained in 4 episodes
• Regularization — 28× in this paper · explained in 3 episodes
• Redundancy — 5× in this paper · explained in 2 episodes
• Variance-Guided CLS Attention — 5× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Hybrid Approach — 3× in this paper · explained in 4 episodes
• Core — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Token Merging: Your ViT But Faster
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
• CipherPrune: Efficient and Scalable Private Transformer Inference
• Power-Softmax: Towards Secure LLM Inference over Encrypted Data

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