mlsanityData to 14 Oct 2026

Federated X-Armed Bandit

arXiv 2205.15268 ↗First posted 30 May 2022stat.ML cs.LGcited by 5

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Abstract (arXiv)

This work establishes the first framework of federated X-armed bandit, where different clients face heterogeneous local objective functions defined on the same domain and are required to collaboratively figure out the global optimum. We propose the first federated algorithm for such problems, named textttFed-PNE. By utilizing the topological structure of the global objective inside the hierarchical partitioning and the weak smoothness property, our algorithm achieves sublinear cumulative regret with respect to both the number of clients and the evaluation budget. Meanwhile, it only requires logarithmic communications between the central server and clients, protecting the client privacy. Experimental results on synthetic functions and real datasets validate the advantages of textttFed-PNE over various centralized and federated baseline algorithms.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 45 references that are in the map. First posted on arXiv 30 May 2022.

Builds on

22 of 45 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018A Tutorial on Bayesian Optimizationcited by 744
  3. 2017On Kernelized Multi-armed Banditscited by 352
  4. 2017Batched Large-scale Bayesian Optimization in High-dimensional Spacescited by 117
  5. 2020Differentially-Private Federated Linear Banditscited by 83
  6. 2018Differentially Private Contextual Linear Banditscited by 82
  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. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  12. 2020Federated Bandit: A Gossiping Approachcited by 55
10 more
  1. 2015Algorithms for Differentially Private Multi-Armed Banditscited by 45
  2. 2021Differentially Private Federated Bayesian Optimization with Distributed Explorationcited by 32
  3. 2020Federated Recommendation System via Differential Privacycited by 26
  4. 2018A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumptioncited by 23
  5. 2021Lipschitz Bandits with Batched Feedbackcited by 21
  6. 2022Federated Neural Banditscited by 17
  7. 2014Online Stochastic Optimization under Correlated Bandit Feedbackcited by 11
  8. 2018A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraintcited by 9
  9. 2021Optimum-statistical Collaboration Towards General and Efficient Black-box Optimizationcited by 6
  10. 2022Federated Online Sparse Decision Makingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023Federated Linear Contextual Bandits with User-level Differential Privacycited by 12
  2. 2023PyXAB -- A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithmscited by 4
  3. 2025Federated Linear Dueling Banditscited by 2
  4. 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-upscited by 1
  5. 2026Instance-dependent Stochastic Lipschitz banditcited by 0