Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning

Documenti analoghi

• Whom to Trust? Adaptive Collaboration in Personalized Federated Learning
  di: Abourayya, Amr, et al.
  Pubblicazione: (2025)
• Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs
  di: Abourayya, Amr, et al.
  Pubblicazione: (2026)
• LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings
  di: Oksuz, Abdullah Caglar, et al.
  Pubblicazione: (2026)
• Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering
  di: Ghasemian, Maryam, et al.
  Pubblicazione: (2024)
• PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research
  di: Jiang, Yuzhou, et al.
  Pubblicazione: (2022)