cs.LG · 2607.11600 Copy arXiv ID · Jul 13, 2026 Save Privacy-Aware Collaborative and Distributed Bayesian Optimization Authors: Aditya Rane , Sathwik Yamana , Paritosh Ramanan , Srikanthan Ramesh , Akash Deep
Organizations: School of Industrial Engineering and Management Oklahoma State University, Stillwater, OK, USA
Abstract 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.
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Yizhao Fan, Wenjian Luo, Jiaojiao Zhang
Private decentralized learning is affected by sampling noise, privacy noise, and decentralized bias under heterogeneous data. We propose Private Recursive Decentralized Optimization (PRDO). PRDO uses recursive estimation with same-batch gradient differences to reduce estimation errors caused by sampling and privacy noise, while its Exact Diffusion component corrects decentralized bias arising from data heterogeneity. Our analysis establishes a nonconvex convergence bound without assuming uniformly bounded data heterogeneity across nodes. It further gives a sufficient condition under which recursive gradient differences yield strictly lower query sensitivity than private Exact Diffusion, together with an example that rigorously satisfies this condition. Experiments show improved accuracy over the evaluated baselines.