[1] Mcmahan H B, Moore E, Ramage, et al. Communication-efficient learning of deep networks form decentralized data[C]//Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017:1-11. [2] Konecny J, Mcmahan H B, Yu F X, et al. Federated learning:strategies for improving communication efficiency[C]//Proceedings of the 30th Annual Conference on Neural Information Processing Systems, 2016:1-10. [3] Yang Q. Challenges of GDPR to AI and countermeasures based on federal transfer learning[J]. Communications of Chinese Association of Artificial Intelligence, 2018, 8:1-8. [4] Yang Q, Liu Y, Chen T J, et al. Federated machine learning:concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2):1-19. [5] Wang S, Tuor T, Salonidis T, et al. Adaptive federated learning in resource constrained edge computing systems[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(6):1205-1221. [6] Liu Y, Liu Y, Liu Z, et al. Federated Forest[J/OL].[2020-06-23]. https://arxiv.org/pdf/1905.10053v1.pdf. [7] Sharma S, Chen K. Poster:privacy-preserving boosting with random linear classifiers[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018:1-13. [8] Sun C, Shrivastava A. Revisiting unreasonable effectiveness of data in deep learning era[C]//2017 IEEE International Conference on Computer Vision (ICCV), 2017, 843-852. [9] Kim H, Park J, Bennis M, et al. On-device federated learning via block chain and its latency analysis[DB/OL].[2020-06-23]. https://arxiv.org/pdf/1808.03949.pdf. [10] Li S Y, Cheng Y, Liu Y, et al. Abnormal client behavior detection in federated learning[DB/OL].[2020-06-23]. https://arxiv.org/pdf/1910.09933.pdf. [11] Zhun L G, Liu Z J, Liu Z J, et al. Deep Leakage from Gradients[DB/OL].[2020-06-23]. https://arxiv.org/pdf/1906.08935. [12] 刘之瑜,张淑芬,刘洋,等.基于图像梯度的数据增广方法[J].应用科学学报, 2021, 39(2):302-311. Liu Z Y, Zhang S F, Liu Y, et al. Data augmentation method based on image gradient[J]. Journal of Applied Science, 2021, 39(2):302-311.(in Chinese) [13] Gao H H, Huang W Q, Yang X X. Applying probabilistic model checking to path planning in an intelligent transportation system using mobility trajectories and their statistical data[J]. Intelligent Automation and Soft Computing, 2019, 25(3):547-559. [14] Gao H H, Huang W Q, Duan Y C, et al. Research on cost-driven services composition in an uncertain environment[J]. Journal of Internet Technology (JIT), 2019, 20(3):755-769. [15] Preuveneers D, Rimmer V, Tsingenopoulos I, et al. Chained anomaly detection models for federated learning:an intrusion detection case study[J]. Applied Sciences, 2018, 8(12):1-12. [16] Brisimi T S, Chen R, Mela T, et al. Federated learning of predictive models from federated Electronic Health Records[J]. International Journal of Medical Informatics, 2018, 112:59-67. [17] Zhang W S, Zhang Y J, Zhai J, et al. Multi-source data fusion using deep learning for smart refrigerators[J]. Computers in Industry, 2018, 95:15-21. [18] Lee J, Wang F, Sun J M, et al. Privacy-preserving patient similarity learning in a federated environment:development and analysis[J]. JMIR Medical Informatics, 2018, 6(2):e20. [19] Shen G J, Han X, Zhou J J, et al. Research on intelligent analysis and depth fusion of multi-source traffic data[J]. IEEE Access, 2018, 6:59329-59335. [20] Liu J, Li T R, Xie P, et al. Urban big data fusion based on deep learning:an overview[J]. Information Fusion, 2020, 53:123-133. [21] Rivest R, Shamir A, Adleman L. A method for obtaining digital signatures and public-key cryptosystems[J]. Communications of the ACM, 1978, 21(2):120-126. [22] 娄悦,施荣华,曹龄兮.基于强认证技术的会话初始协议安全认证模型[J].计算机应用, 2006, 30(10):2332-2335. Lou Y, Shi R H, Cao L X. Security authenti cation model of session initiation protocol based on strong authentication technology[J]. Journal of Computer Applications, 2006, 30(10):2332-2335.(in Chinese) [23] 鲁莹,郑少智. Stacking学习与一般集成方法的比较研究[J/OL].中国科技论文在线精品论文, 2018, 11(4):372-379. Lu Y, Zheng S Z. A comparative study of stacking learning and general integration methods[J/OL]. Highlights of Science paper Online, 2018, 11(4):372-379.(in Chinese) [24] 陈玉昇,杨燕华,林萌,等.基于深度学习神经网络的核电厂故障诊断技术[J].上海交通大学学报, 2018, 52(S1):58-61. Chen Y S, Yang Y H, Lin M, et al. Nuclear power plant fault diagnosis technology based on deep learning neural network[J]. Journal of Shanghai JiaoTong University, 2018, 52(Suppl.1):58-61.(in Chinese) |