mlsanityData to 7 Oct 2026

Collaborative Learning in Kernel-based Bandits for Distributed Users

arXiv 2207.07948 ↗First posted 16 Jul 2022stat.ML cs.LGcited by 6

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Abstract (arXiv)

We study collaborative learning among distributed clients facilitated by a central server. Each client is interested in maximizing a personalized objective function that is a weighted sum of its local objective and a global objective. Each client has direct access to random bandit feedback on its local objective, but only has a partial view of the global objective and relies on information exchange with other clients for collaborative learning. We adopt the kernel-based bandit framework where the objective functions belong to a reproducing kernel Hilbert space. We propose an algorithm based on surrogate Gaussian process (GP) models and establish its order-optimal regret performance (up to polylogarithmic factors). We also show that the sparse approximations of the GP models can be employed to reduce the communication overhead across clients.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 44 of its 64 references that are in the map. First posted on arXiv 16 Jul 2022.

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44 of 64 references.

References · 44

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2013Gaussian Processes for Big Datacited by 377
  7. 2020Adaptive Personalized Federated Learningcited by 353
  8. 2017On Kernelized Multi-armed Banditscited by 348
  9. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  10. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  11. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  12. 2018Gaussian Processes and Kernel Methods: A Review on Connections and Equivalencescited by 188
32 more
  1. 2013Finite-Time Analysis of Kernelised Contextual Banditscited by 177
  2. 2020On Information Gain and Regret Bounds in Gaussian Process Banditscited by 156
  3. 2019Federated Evaluation of On-device Personalizationcited by 147
  4. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  5. 2020Differentially-Private Federated Linear Banditscited by 83
  6. 2017Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimizationcited by 77
  7. 2020Federated Bayesian Optimization via Thompson Samplingcited by 77
  8. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  9. 2021Federated Linear Contextual Banditscited by 61
  10. 2021Federated Multi-Armed Banditscited by 59
  11. 2021Gaussian Process Bandit Optimization with Few Batchescited by 57
  12. 2021Optimal Order Simple Regret for Gaussian Process Banditscited by 52
  13. 2016Distributed Clustering of Linear Bandits in Peer to Peer Networkscited by 50
  14. 2019Social Learning in Multi Agent Multi Armed Banditscited by 48
  15. 2013Distributed Exploration in Multi-Armed Banditscited by 46
  16. 2020A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performancecited by 38
  17. 2015On Distributed Cooperative Decision-Making in Multiarmed Banditscited by 33
  18. 2018Regret bounds for meta Bayesian optimization with an unknown Gaussian process priorcited by 31
  19. 2014Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameterscited by 30
  20. 2020Federated Recommendation System via Differential Privacycited by 26
  21. 2020Kernel Methods for Cooperative Multi-Agent Contextual Banditscited by 21
  22. 2017Gaussian Process bandits with adaptive discretizationcited by 20
  23. 2022Near-Optimal Collaborative Learning in Banditscited by 18
  24. 2021Connections and Equivalences between the Nystr"om Method and Sparse Variational Gaussian Processescited by 17
  25. 2022Communication Efficient Distributed Learning for Kernelized Contextual Banditscited by 15
  26. 2017Second-Order Kernel Online Convex Optimization with Adaptive Sketchingcited by 13
  27. 2021Open Problem: Tight Online Confidence Intervals for RKHS Elementscited by 13
  28. 2021Collaborative Pure Exploration in Kernel Banditcited by 13
  29. 2022Distributed Contextual Linear Bandits with Minimax Optimal Communication Costcited by 13
  30. 2021Lenient Regret and Good-Action Identification in Gaussian Process Banditscited by 12
  31. 2019Statistical and Computational Trade-Offs in Kernel K-Meanscited by 7
  32. 2021Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Banditscited by 4

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Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 1.

6 papers.

  1. 2023Federated Domain Generalization: A Surveycited by 25
  2. 2023Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiencycited by 16
  3. 2023Distributed Optimization via Kernelized Multi-armed Banditscited by 0
  4. 2023Optimal Regret Bounds for Collaborative Learning in Banditscited by 0
  5. 2024Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomnesscited by 0
  6. 2025Differential Privacy in Kernelized Contextual Bandits via Random Projectionscited by 0