Research map / Federated learning and privacy / Differential privacy
Differentially private Bayesian learning on distributed data
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add data projection to distributed Bayesian linear regression (ablation) | Distributed Bayesian linear regression (DDP) · UCI Wine Quality and Abalone | MAE | Helped |
| Add data projection to distributed Bayesian linear regression (ablation) | Distributed Bayesian linear regression (DDP) · GDSC | Spearman's rank correlation | Helped |
| Use the distributed scheme instead of a trusted aggregator | Bayesian linear regression · UCI Wine Quality and Abalone | MAE | No clear effect |
| Use the distributed scheme instead of a trusted aggregator | Bayesian linear regression · GDSC | Spearman's rank correlation | No clear effect |
Closest papers by meaning
- DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference (ICML 2023 · similarity 0.91)
- On Inherent Privacy of Posterior Sampling: A Unified R\'enyi-Divergence Framework (NeurIPS 2026 · similarity 0.90)
- Privacy Guarantees in Posterior Sampling under Contamination (NeurIPS 2026 · similarity 0.89)
- Optimal Differentially Private Model Training with Public Data (ICML 2024 · similarity 0.89)
- Learning from End User Data with Shuffled Differential Privacy over Kernel Densities (ICLR 2025 · similarity 0.88)
- Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy (ICLR 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.