Privacy-Aware Collaborative and Distributed Bayesian Optimization
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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.
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.
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References · 12
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2016Deep Learning with Differential Privacycited by 2,728
- 2019Deep Leakage from Gradientscited by 1,021
- 2018A Tutorial on Bayesian Optimizationcited by 744
- 2019Differentially Private Meta-Learningcited by 41
- 2021Meta-Learning Reliable Priors in the Function Spacecited by 23
- 2022Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practicecited by 11
- 2020Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?cited by 9
- 2023Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Designcited by 9
- 2025Collaborative Bayesian Optimization via Wasserstein Barycenterscited by 7
- 2025Differential Privacy: Gradient Leakage Attacks in Federated Learning Environmentscited by 3
- 2026Collaborative Contextual Bayesian Optimizationcited by 1
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