mlsanityData to 14 Oct 2026

Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective

arXiv 2411.07523 ↗First posted 12 Nov 2024cs.LG stat.MLcited by 5

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Abstract (arXiv)

We focus on collaborative and federated black-box optimization (BBOpt), where agents optimize their heterogeneous black-box functions through collaborative sequential experimentation. From a Bayesian optimization perspective, we address the fundamental challenges of distributed experimentation, heterogeneity, and privacy within BBOpt, and propose three unifying frameworks to tackle these issues: (i) a global framework where experiments are centrally coordinated, (ii) a local framework that allows agents to make decisions based on minimal shared information, and (iii) a predictive framework that enhances local surrogates through collaboration to improve decision-making. We categorize existing methods within these frameworks and highlight key open questions to unlock the full potential of federated BBOpt. Our overarching goal is to shift federated learning from its predominantly descriptive/predictive paradigm to a prescriptive one, particularly in the context of BBOpt - an inherently sequential decision-making problem.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 12 Nov 2024.

Builds on

23 of 33 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018A Tutorial on Bayesian Optimizationcited by 744
  3. 2020Federated Learning with Matched Averagingcited by 564
  4. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  5. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  6. 2019Fair Resource Allocation in Federated Learningcited by 394
  7. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  8. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 312
  9. 2015Batch Bayesian Optimization via Local Penalizationcited by 104
  10. 2020Federated Bayesian Optimization via Thompson Samplingcited by 77
  11. 2022πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimizationcited by 70
  12. 2019Uniform Error Bounds for Gaussian Process Regression with Application to Safe Controlcited by 64
11 more
  1. 2020Fast-Convergent Federated Learningcited by 61
  2. 2021Differentially Private Federated Bayesian Optimization with Distributed Explorationcited by 32
  3. 2021Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learningcited by 23
  4. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  5. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  6. 2022Personalized PCA: Decoupling Shared and Unique Featurescited by 17
  7. 2022Federated Data Analytics: A Study on Linear Modelscited by 9
  8. 2023Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Designcited by 9
  9. 2023Personalized Dictionary Learning for Heterogeneous Datasetscited by 5
  10. 2023Heterogeneous Matrix Factorization: When Features Differ by Datasetscited by 5
  11. 2024Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Componentscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimizationcited by 14
  2. 2026Collaborative Contextual Bayesian Optimizationcited by 1
  3. 2025ARCO-BO: Adaptive Resource-aware COllaborative Bayesian Optimization for Heterogeneous Multi-Agent Designcited by 0
  4. 2026Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization taskscited by 0
  5. 2026GUIDE-FBO: Guidance via Uncertainty Intervention and Distributional Exchange for Federated Bayesian Optimizationcited by 0