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Federated Recommendation System via Differential Privacy

arXiv 2005.06670 ↗First posted 14 May 2020cs.LG cs.IT math.ITcited by 26

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Abstract (arXiv)

In this paper, we are interested in what we term the federated private bandits framework, that combines differential privacy with multi-agent bandit learning. We explore how differential privacy based Upper Confidence Bound (UCB) methods can be applied to multi-agent environments, and in particular to federated learning environments both in `master-worker' and `fully decentralized' settings. We provide a theoretical analysis on the privacy and regret performance of the proposed methods and explore the tradeoffs between these two.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 7; 2024: 4; 2025: 2; 2026: 1.

26 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. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  5. 2022A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Banditscited by 28
  6. 2022Federated Neural Banditscited by 17
  7. 2024Poisoning Federated Recommender Systems with Fake Userscited by 15
  8. 2023Federated Linear Contextual Bandits with User-level Differential Privacycited by 12
  9. 2021Privacy-Preserving Communication-Efficient Federated Multi-Armed Banditscited by 11
  10. 2024A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacycited by 11
  11. 2022Collaborative Learning in Kernel-based Bandits for Distributed Userscited by 6
  12. 2022Federated X-Armed Banditcited by 5
14 more
  1. 2024Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learningcited by 4
  2. 2025Gap-Dependent Bounds for Federated Q-learningcited by 4
  3. 2022Federated Online Sparse Decision Makingcited by 3
  4. 2023Byzantine-Resilient Decentralized Multi-Armed Banditscited by 3
  5. 2023Federated Linear Bandits with Finite Adversarial Actionscited by 3
  6. 2021Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Banditscited by 2
  7. 2025Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learningcited by 2
  8. 2023Federated Learning for Heterogeneous Bandits with Unobserved Contextscited by 1
  9. 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-upscited by 1
  10. 2022Vertical Federated Linear Contextual Banditscited by 0
  11. 2023DRIFT: A Federated Recommender System with Implicit Feedback on the Itemscited by 0
  12. 2023Reward Teaching for Federated Multi-armed Banditscited by 0
  13. 2023Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspacescited by 0
  14. 2026FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharingcited by 0