Differentially Private Federated Learning without Noise Addition: When is it Possible?

Documenti analoghi

• SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models
  di: Zhang, Jiang, et al.
  Pubblicazione: (2025)
• Location Leakage in Federated Signal Maps
  di: Bakopoulou, Evita, et al.
  Pubblicazione: (2021)
• All Rivers Run to the Sea: Private Learning with Asymmetric Flows
  di: Niu, Yue, et al.
  Pubblicazione: (2023)
• Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
  di: Jin, Huitong, et al.
  Pubblicazione: (2024)
• Preserving Privacy and Utility in LLM-Based Product Recommendations
  di: Khezresmaeilzadeh, Tina, et al.
  Pubblicazione: (2025)