Fed-AttGRU Privacy-preserving Federated Interest Recommendation

Author:

Wan Jun1ORCID,Chi Cheng2ORCID,Yu Haoyuan3ORCID,Liu Yang2ORCID,Xu Xiangrui3ORCID,Lyu Hongmei3ORCID,Wang Wei3ORCID

Affiliation:

1. Shuohuang Railway Development Co.,Ltd, National Energy Group, China

2. China Academy of Information and Communications Technology, China

3. Beijing Jiaotong University, China

Funder

the National Key R&D Program of China

Beijing Natural Science Foundation

Publisher

ACM

Reference20 articles.

1. Michele Bugliesi, Bart Preneel, Vladimiro Sassone, and Ingo Wegener. 2006. Automata, Languages and Programming: 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part I. Vol. 4051. Springer.

2. DeepMove

3. PMF: A privacy-preserving human mobility prediction framework via federated learning;Feng Jie;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies,2020

4. Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017).

5. CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles

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