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Differentially-Private Federated Linear Bandits

arXiv 2010.11425 ↗First posted 22 Oct 2020cs.LG cs.CR cs.MAcited by 83

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Abstract (arXiv)

The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning. In this paper, we study this in context of the contextual linear bandit: we consider a collection of agents cooperating to solve a common contextual bandit, while ensuring that their communication remains private. For this problem, we devise textscFedUCB, a multiagent private algorithm for both centralized and decentralized (peer-to-peer) federated learning. We provide a rigorous technical analysis of its utility in terms of regret, improving several results in cooperative bandit learning, and provide rigorous privacy guarantees as well. Our algorithms provide competitive performance both in terms of pseudoregret bounds and empirical benchmark performance in various multi-agent settings.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 24; 2023: 24; 2024: 10; 2025: 8; 2026: 2.

83 papers.

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  1. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  2. 2021Federated Linear Contextual Banditscited by 61
  3. 2021Federated Multi-Armed Banditscited by 59
  4. 2021Fault-Tolerant Federated Reinforcement Learning with Theoretical Guaranteecited by 52
  5. 2021Differentially Private Federated Bayesian Optimization with Distributed Explorationcited by 32
  6. 2021Asynchronous Upper Confidence Bound Algorithms for Federated Linear Banditscited by 31
  7. 2021Multi-Agent Multi-Armed Bandits with Limited Communicationcited by 29
  8. 2022A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Banditscited by 28
  9. 2021Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Banditscited by 25
  10. 2021Differentially Private Multi-Armed Bandits in the Shuffle Modelcited by 24
  11. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  12. 2021Exploiting Heterogeneity in Robust Federated Best-Arm Identificationcited by 20
38 more
  1. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  2. 2022Near-Optimal Collaborative Learning in Banditscited by 18
  3. 2022Federated Neural Banditscited by 17
  4. 2022Communication Efficient Distributed Learning for Kernelized Contextual Banditscited by 15
  5. 2023On Differentially Private Federated Linear Contextual Banditscited by 14
  6. 2021Collaborative Pure Exploration in Kernel Banditcited by 13
  7. 2022Communication Efficient Federated Learning for Generalized Linear Banditscited by 13
  8. 2022Distributed Contextual Linear Bandits with Minimax Optimal Communication Costcited by 13
  9. 2023Federated Linear Contextual Bandits with User-level Differential Privacycited by 12
  10. 2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 12
  11. 2021Communication Efficient Parallel Reinforcement Learningcited by 10
  12. 2021A Payload Optimization Method for Federated Recommender Systemscited by 10
  13. 2022Federated Online Clustering of Banditscited by 10
  14. 2022Linear Stochastic Bandits over a Bit-Constrained Channelcited by 9
  15. 2023Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximationcited by 9
  16. 2022Differentially Private Linear Bandits with Partial Distributed Feedbackcited by 8
  17. 2022Federated Best Arm Identification with Heterogeneous Clientscited by 8
  18. 2022Byzantine-Robust Federated Linear Banditscited by 7
  19. 2023Doubly Adversarial Federated Banditscited by 7
  20. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  21. 2023Federated Online and Bandit Convex Optimizationcited by 7
  22. 2022Federated Multi-Armed Bandits Under Byzantine Attackscited by 6
  23. 2022Collaborative Learning in Kernel-based Bandits for Distributed Userscited by 6
  24. 2023On-Demand Communication for Asynchronous Multi-Agent Banditscited by 6
  25. 2023Pure Exploration in Asynchronous Federated Banditscited by 6
  26. 2024Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Userscited by 6
  27. 2024FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clientscited by 6
  28. 2022Federated X-Armed Banditcited by 5
  29. 2022Dynamic Global Sensitivity for Differentially Private Contextual Banditscited by 5
  30. 2022Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Boundscited by 4
  31. 2023Collaborative Multi-Agent Heterogeneous Multi-Armed Banditscited by 4
  32. 2022Federated Online Sparse Decision Makingcited by 3
  33. 2022Differentially Private Federated Combinatorial Bandits with Constraintscited by 3
  34. 2022Lifelong Bandit Optimization: No Prior and No Regretcited by 3
  35. 2023Incentivized Communication for Federated Banditscited by 3
  36. 2023Byzantine-Resilient Decentralized Multi-Armed Banditscited by 3
  37. 2023Federated Linear Bandits with Finite Adversarial Actionscited by 3
  38. 2024Federated Linear Contextual Bandits with Heterogeneous Clientscited by 3

50 most cited of 83 All 83 (JSON)