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Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

arXiv 2110.01463 ↗First posted 4 Oct 2021cs.LGcited by 31

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Abstract (arXiv)

Linear contextual bandit is a popular online learning problem. It has been mostly studied in centralized learning settings. With the surging demand of large-scale decentralized model learning, e.g., federated learning, how to retain regret minimization while reducing communication cost becomes an open challenge. In this paper, we study linear contextual bandit in a federated learning setting. We propose a general framework with asynchronous model update and communication for a collection of homogeneous clients and heterogeneous clients, respectively. Rigorous theoretical analysis is provided about the regret and communication cost under this distributed learning framework; and extensive empirical evaluations demonstrate the effectiveness of our solution.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 4 Oct 2021.

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17 of 35 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2017Federated Multi-Task Learningcited by 669
  5. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  6. 2014Online Clustering of Banditscited by 99
  7. 2020Differentially-Private Federated Linear Banditscited by 83
  8. 2015Communication Complexity of Distributed Convex Learning and Optimizationcited by 59
  9. 2016Distributed Clustering of Linear Bandits in Peer to Peer Networkscited by 50
  10. 2013A Gang of Banditscited by 49
  11. 2019Improved Algorithm on Online Clustering of Banditscited by 38
  12. 2019Perturbed-History Exploration in Stochastic Linear Banditscited by 28
5 more
  1. 2020Fast Distributed Bandits for Online Recommendation Systemscited by 18
  2. 2019Laplacian-regularized graph bandits: Algorithms and theoretical analysiscited by 17
  3. 2014Distributed convex optimization via continuous-time coordination algorithms with discrete-time communicationcited by 11
  4. 2020Unifying Clustered and Non-stationary Banditscited by 11
  5. 2018Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communicationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 10; 2024: 8; 2025: 4; 2026: 2.

31 papers.

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  1. 2022A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Banditscited by 28
  2. 2022Federated Neural Banditscited by 17
  3. 2022Communication Efficient Distributed Learning for Kernelized Contextual Banditscited by 15
  4. 2021Collaborative Pure Exploration in Kernel Banditcited by 13
  5. 2022Communication Efficient Federated Learning for Generalized Linear Banditscited by 13
  6. 2023Federated Linear Contextual Bandits with User-level Differential Privacycited by 12
  7. 2024Federated Combinatorial Multi-Agent Multi-Armed Banditscited by 11
  8. 2022Federated Online Clustering of Banditscited by 10
  9. 2023Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximationcited by 9
  10. 2023Federated Online and Bandit Convex Optimizationcited by 7
  11. 2023On-Demand Communication for Asynchronous Multi-Agent Banditscited by 6
  12. 2023Pure Exploration in Asynchronous Federated Banditscited by 6
19 more
  1. 2024Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Userscited by 6
  2. 2024FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clientscited by 6
  3. 2023Incentivized Communication for Federated Banditscited by 3
  4. 2023Federated Linear Bandits with Finite Adversarial Actionscited by 3
  5. 2024Federated Linear Contextual Bandits with Heterogeneous Clientscited by 3
  6. 2024Incentivized Truthful Communication for Federated Banditscited by 2
  7. 2025Federated Linear Dueling Banditscited by 2
  8. 2023Communication-Efficient Federated Non-Linear Bandit Optimizationcited by 1
  9. 2023Harnessing the Power of Federated Learning in Federated Contextual Banditscited by 1
  10. 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-upscited by 1
  11. 2026Batched Stochastic Linear Bandits with 1-Bit Communication Constraintscited by 1
  12. 2022Vertical Federated Linear Contextual Banditscited by 0
  13. 2023Reward Teaching for Federated Multi-armed Banditscited by 0
  14. 2024FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learningcited by 0
  15. 2024Multi-Agent Best Arm Identification in Stochastic Linear Banditscited by 0
  16. 2025On the optimal regret of collaborative personalized linear banditscited by 0
  17. 2025Decentralized Contextual Bandits with Network Adaptivitycited by 0
  18. 2025Anomaly Detection in Networked Banditscited by 0
  19. 2026Scaling Federated Linear Contextual Bandits via Sketchingcited by 0