Metraipolis › archive › dc_e358eaee0afe

GUIDE-FBO: Guidance via Uncertainty Intervention and Distributional Exchange for Federated Bayesian Optimization — arXiv:2609.35038

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35038
License (as archived): CC0-1.0
Archived: 2026-10-07T22:41:05.774Z
SHA-256 of the archived text: a7668ddba63f15841995d65d7206d25d785975f4a2d5b2aedb634a514b518784
Stable URL of this page: https://metraipolis.com/documento/dc_e358eaee0afe

How to cite

GUIDE-FBO: Guidance via Uncertainty Intervention and Distributional Exchange for Federated Bayesian Optimization — arXiv:2609.35038 — arXiv (en), https://arxiv.org/abs/2609.35038 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e358eaee0afe (Metraipolis archive dc_e358eaee0afe, sha256 a7668ddba63f15841995d65d7206d25d785975f4a2d5b2aedb634a514b518784)

BibTeX

@misc{dce358eaee0afe,
  title = {GUIDE-FBO: Guidance via Uncertainty Intervention and Distributional Exchange for Federated Bayesian Optimization — arXiv:2609.35038},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35038},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e358eaee0afe; sha256 a7668ddba63f15841995d65d7206d25d785975f4a2d5b2aedb634a514b518784},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35038. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Federated Bayesian Optimization (FBO) enables distributed agents to collaboratively optimize expensive black-box objectives without sharing raw local observations. However, effective knowledge transfer remains challenging under communication constraints and task heterogeneity. We propose GUIDE-FBO, in which agents exchange compact distributions over the locations of their respective optima inferred from local Gaussian process (GP) posteriors, rather than raw observations, query points, or surrogate parameters. The server merges and reweights these distributional components before returning a subset to each agent. Each agent then constructs a Federated Interventional GP (FI-GP), which preserves the local posterior mean and spatially rescales its covariance for local decision making. For the upper confidence bound (UCB) instantiation, GUIDE-UCB, we prove that any bounded FI-GP uncertainty intervention preserves the leading-order cumulative regret rate of standard GP-UCB. When the transferred distributions place greater support near an optimum than in a suboptimal region, selecting the latter requires greater local posterior uncertainty. Experiments on 12 synthetic benchmarks and three real-world optimization tasks show that GUIDE-FBO remains effective across settings ranging from homogeneous to severely heterogeneous. Ablation results highlight the importance of spatially localized uncertainty intervention, while the communication analysis shows that GUIDE-FBO exchanges only compact distributional messages.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.35038 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:14:28.892Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).