A trusted recommendation scheme for privacy protection based on federated learning


With the convergence of the era of global news and the era of big data, the daily amount of news sent to the world is exploding. Users also face the problem of information overloads when they get massive information, which leads to how cloud servers push personalized data to users among massive data have become the focus of news companies. In order to obtain the push accuracy, the traditional recommendation system often makes deep mining of users’ privacy data, which makes users’ privacy cannot be guaranteed. In order to solve the above problems, this paper proposes a collaborative filtering algorithm recommendation system based on federated learning on end-edge-cloud. The exposure of data privacy was further prevented by adding Laplace noise to the training model through differential privacy technology. Finally, the training model and recommendation information is stored to the blockchain network to provide permanent storage, evidence chain and real-time traceability services.On the premise of protecting data privacy, this system provides cloud server with solutions to alleviate computing pressure, bandwidth pressure and improve news push accuracy through end-edge-cloud distributed learning.

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This research work is supposed by the National Key R&D Program of China (2018YFB1201500), National Natural Science Founds of China (62072368, 61773313, 61702411), National Natural Science Founds of Shaanxi (2017JQ6020, 2016JQ6041), Key Research and Development Program of Shaanxi Province (2020GY-039, 2017ZDXMGY-098, 2019TD-014)

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Correspondence to Xinhong Hei.

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Wang, Y., Tian, Y., Yin, X. et al. A trusted recommendation scheme for privacy protection based on federated learning. CCF Trans. Netw. 3, 218–228 (2020). https://doi.org/10.1007/s42045-020-00045-8

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  • Federated learning
  • Blockchain
  • Differential privacy
  • Recommendation system