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Vertical Federated Linear Contextual Bandits

arXiv 2210.11050 ↗First posted 20 Oct 2022cs.LG stat.MLcited by 0

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Abstract (arXiv)

In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i.e., contextual information is vertically distributed over different departments. This problem remains largely unexplored in the research community. To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask mechanism(O3M) for encrypting local contextual information while avoiding expensive conventional cryptographic techniques. We further apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instantiate two practical protocols for online recommendation under the vertical federated setting. The proposed protocols can perfectly recover the service quality of centralized bandit algorithms while achieving a satisfactory runtime efficiency, which is theoretically proved and analyzed in this paper. By conducting extensive experiments on both synthetic and real-world datasets, we show the superiority of the proposed method in terms of privacy protection and recommendation performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 20 Oct 2022.

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References · 19

  1. 2019Introduction to Multi-Armed Banditscited by 475
  2. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  3. 2017Fast Matrix Factorization for Online Recommendation with Implicit Feedbackcited by 100
  4. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  5. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  6. 2020Differentially-Private Federated Linear Banditscited by 83
  7. 2018Differentially Private Contextual Linear Banditscited by 82
  8. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  9. 2021Federated Linear Contextual Banditscited by 61
  10. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  11. 2020Locally Differentially Private (Contextual) Bandits Learningcited by 52
  12. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
7 more
  1. 2021Asynchronous Upper Confidence Bound Algorithms for Federated Linear Banditscited by 31
  2. 2021Generalized Linear Bandits with Local Differential Privacycited by 29
  3. 2020Federated Recommendation System via Differential Privacycited by 26
  4. 2020Federated Residual Learningcited by 23
  5. 2021Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Datacited by 18
  6. 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Datacited by 9
  7. 2019Privacy-Preserving Banditscited by 6

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