# Privacy-Aware Collaborative and Distributed Bayesian Optimization

arXiv: https://arxiv.org/abs/2607.11600 · HTML: https://mlsanity.com/paper/2607.11600/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Differentially Private Meta-Learning](https://mlsanity.com/paper/1909.05830/) (2019)
- [Meta-Learning Reliable Priors in the Function Space](https://mlsanity.com/paper/2106.03195/) (2021)
- [Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice](https://mlsanity.com/paper/2211.07206/) (2022)
- [Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?](https://mlsanity.com/paper/2012.08240/) (2020)
- [Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design](https://mlsanity.com/paper/2306.14348/) (2023)
- [Collaborative Bayesian Optimization via Wasserstein Barycenters](https://mlsanity.com/paper/2504.10770/) (2025)
- [Differential Privacy: Gradient Leakage Attacks in Federated Learning Environments](https://mlsanity.com/paper/2510.23931/) (2025)
- [Collaborative Contextual Bayesian Optimization](https://mlsanity.com/paper/2604.18912/) (2026)

## Abstract (arXiv)

We propose a collaborative meta-learning framework for distributed Bayesian optimization matching centralized performance without raw-data exchange. We show gradient sharing leaks client observations, with leakage worsening as the search converges and queries concentrate near the optimum. We evaluate a differentially private defense and characterize its privacy-utility trade-off.