{"ID":6537469,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11600","arxiv_id":"2607.11600","title":"Privacy-Aware Collaborative and Distributed Bayesian Optimization","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.","short_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 p...","url_abs":"https://arxiv.org/abs/2607.11600","url_pdf":"https://arxiv.org/pdf/2607.11600v1","authors":"[\"Aditya Rane\",\"Sathwik Yamana\",\"Paritosh Ramanan\",\"Srikanthan Ramesh\",\"Akash Deep\"]","published":"2026-07-13T14:25:04Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.ME\"]","methods":"[]","has_code":false}