Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation

Author:

Cao Zeyu1ORCID,Liang Zhipeng2ORCID,Wu Bingzhe3ORCID,Zhang Shu3ORCID,Li Hangyu3ORCID,Wen Ouyang3ORCID,Rong Yu3ORCID,Zhao Peilin3ORCID

Affiliation:

1. University of Cambridge, Cambridge, United Kingdom

2. Hong Kong University of Science and Technology, Hong Kong, China

3. Tencent AI Lab, Shenzhen, China

Publisher

ACM

Reference41 articles.

1. Yasin Abbasi-Yadkori , Dávid Pál , and Csaba Szepesvári . 2011. Improved Algorithms for Linear Stochastic Bandits . In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011 . Proceedings of a meeting held 12-14 December 2011, Granada, Spain, John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, and Kilian Q. Weinberger (Eds .). 2312--2320. https://proceedings.neurips.cc/paper/2011/hash/e1d5be1c7f2f456670de3d53c7b54f4a-Abstract.html Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári. 2011. Improved Algorithms for Linear Stochastic Bandits. In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain, John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, and Kilian Q. Weinberger (Eds.). 2312--2320. https://proceedings.neurips.cc/paper/2011/hash/e1d5be1c7f2f456670de3d53c7b54f4a-Abstract.html

2. Alekh Agarwal , John Langford , and Chen-Yu Wei . 2020. Federated residual learning. arXiv preprint arXiv:2003.12880 ( 2020 ). Alekh Agarwal, John Langford, and Chen-Yu Wei. 2020. Federated residual learning. arXiv preprint arXiv:2003.12880 (2020).

3. Shipra Agrawal and Navin Goyal . 2013 . Thompson sampling for contextual bandits with linear payoffs . In International conference on machine learning. PMLR, 127--135 . Shipra Agrawal and Navin Goyal. 2013. Thompson sampling for contextual bandits with linear payoffs. In International conference on machine learning. PMLR, 127--135.

4. Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

5. Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data

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