Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives

Documenti analoghi

• EQO: Exploring Ultra-Efficient Private Inference with Winograd-Based Protocol and Quantization Co-Optimization
  di: Zeng, Wenxuan, et al.
  Pubblicazione: (2024)
• CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing
  di: Zhou, Yifan, et al.
  Pubblicazione: (2025)
• PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization
  di: Xu, Tianshi, et al.
  Pubblicazione: (2024)
• UFO: Unlocking Ultra-Efficient Quantized Private Inference with Protocol and Algorithm Co-Optimization
  di: Zeng, Wenxuan, et al.
  Pubblicazione: (2026)
• Ariadne: a Privacy-Preserving Communication Protocol
  di: Fressancourt, Antoine, et al.
  Pubblicazione: (2024)