graduation_project/ref.bib

% （1）专著中的文献
% [序号] 作者.专著名称.版本(第１版不加标注).出版者.出版年:参考页码.


% (2)期刊中的文献
% [序号] 作者.文献名称.期刊名称.卷号（期号）.年,月:页码范围.

@article{Facebook2020Federated,
  title   = {The future of digital health with federated learning},
  author  = {Rieke N, Hancox J, Li W},
  journal = {NPJ digital medicine},
  pages   = {1-7},
  year    = {2020}
}

@article{yang2018applied,
  title   = {Applied federated learning: Improving google keyboard query suggestions},
  author  = {Yang, T and Andrew, G and Eichner, H and others},
  journal = {arXiv preprint arXiv:1812.02903},
  year    = {2018}
}

@article{kim2019blockchained,
  title   = {Blockchained on-device federated learning},
  author  = {Kim, H and Park, J and Bennis, M and others},
  journal = {IEEE Communications Letters},
  year    = {2019},
  volume  = {24},
  number  = {6},
  pages   = {1279-1283}
}

@article{zhao2018federated,
  title   = {Federated learning with non-iid data},
  author  = {Zhao, Y and Li, M and Lai, L and others},
  journal = {arXiv preprint arXiv:1806.00582},
  year    = {2018}
}

@article{zhang2021survey,
  title   = {A survey on federated learning},
  author  = {Zhang, C and Xie, Y and Bai, H and others},
  journal = {Knowledge-Based Systems},
  year    = {2021},
  volume  = {216},
  pages   = {106775}
}

@article{yang2019federated,
  title   = {Federated machine learning: Concept and applications},
  author  = {Yang, Qiang and Liu, Yang and Chen, Tianjian and others},
  journal = {ACM Transactions on Intelligent Systems and Technology},
  volume  = {10},
  number  = {2},
  pages   = {1--19},
  year    = {2019}
}

@inproceedings{vaidya2002privacy,
  title        = {Privacy preserving association rule mining in vertically partitioned data},
  author       = {Vaidya, J and Clifton, C},
  booktitle    = {Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages        = {639--644},
  year         = {2002},
  organization = {ACM}
}

@article{pan2010survey,
  title     = {A survey on transfer learning},
  author    = {Pan, S J and Yang, Qiang},
  journal   = {IEEE Transactions on Knowledge and Data Engineering},
  volume    = {22},
  number    = {10},
  pages     = {1345--1359},
  year      = {2010},
  publisher = {IEEE}
}

@article{mcmahan2016federated,
  title   = {Federated learning of deep networks using model averaging},
  author  = {McMahan, H Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and others},
  journal = {arXiv preprint arXiv:1602.05629},
  year    = {2016}
}

@article{geyer2017differentially,
  title   = {Differentially private federated learning: a client level perspective},
  author  = {Geyer, R C and Klein, T and Nabi, M},
  journal = {arXiv preprint arXiv:1712.07557},
  year    = {2017}
}

@inproceedings{pan2019research,
  title     = {不同数据分布的联邦机器学习技术研究},
  author    = {潘碧莹 and 丘海华 and 张家伦},
  booktitle = {5G网络创新研讨会 (2019) 论文集},
  year      = {2019}
}

@article{liu2020survey,
  title   = {机器学习的隐私保护研究综述},
  author  = {刘俊旭 and 孟小峰},
  journal = {计算机研究与发展},
  volume  = {57},
  number  = {2},
  pages   = {346},
  year    = {2020}
}

@inproceedings{dwork2006calibrating,
  title     = {Calibrating noise to sensitivity in private data analysis},
  author    = {Dwork, C and Mc-Sherry, F and Nissim, K and others},
  booktitle = {Theory of Cryptography Conference},
  pages     = {265--284},
  year      = {2006}
}

@article{su2019survey,
  title   = {安全多方计算技术与应用综述},
  author  = {苏冠通 and 徐茂桐},
  journal = {信息通信技术与政策},
  number  = {5},
  pages   = {19-22},
  year    = {2019}
}

@article{dolev1983security,
  title   = {On the security of public key protocols},
  author  = {Dolev, D and Yao, A},
  journal = {IEEE Transactions on Information Theory},
  volume  = {29},
  number  = {2},
  pages   = {198--208},
  year    = {1983}
}

@article{rivest1978method,
  title   = {A method for obtaining digital signatures and public-key cryptosystems},
  author  = {Rivest, R L and Shamir, A and Adleman, L},
  journal = {Communications of the ACM},
  volume  = {21},
  number  = {2},
  pages   = {120--126},
  year    = {1978}
}

@inproceedings{leroy2019federated,
  title     = {Federated Learning for Keyword Spotting},
  author    = {Leroy, D and Coucke, A and Lavril, T and others},
  booktitle = {ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages     = {6341--6345},
  year      = {2019}
}

@article{chen2019federated,
  title   = {Federated Learning of out-of-Vocabulary Words},
  author  = {Chen, M and Mathews, R and Ouyang, TY and others},
  journal = {arXiv preprint arXiv:1812.02903},
  year    = {2019}
}

@article{hard2018federated,
  title   = {Federated Learning for Mobile Keyboard Prediction},
  author  = {Hard, A and Rao, K and Mathews, R and others},
  journal = {arXiv preprint arXiv:1811.03604},
  year    = {2018}
}

@article{feng2020pmf,
  title   = {PMF: A Privacy-preserving Human Mobility Prediction Framework Via Federated Learning},
  author  = {Feng, J and Rong, C and Sun, F and others},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume  = {2020},
  number  = {1}
}

@inproceedings{sozinov2018human,
  title     = {Human Activity Recognition Using Federated Learning},
  author    = {Sozinov, K and Vlassov, V and Girdzijauskas, S},
  booktitle = {2018 IEEE Intl Conf on Parallel \& Distributed Processing with Applications, Ubiquitous Computing \& Communications, Big Data \& Cloud Computing, Social Computing \& Networking, Sustainable Computing \& Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)},
  year      = {2018}
}

@inproceedings{yu2020learning,
  title     = {Learning Context-Aware Policies from Multiple Smart Homes Via Federated Multi-task Learning},
  author    = {Yu, T and Li, T and Sun, Y and others},
  booktitle = {2020 IEEE/ACM Fifth International Conference on Internet-of-things Design and Implementation (IoTDI)},
  year      = {2020}
}

@inproceedings{aivodji2019lotfla,
  title     = {LOFTLA: A Secured and Privacy-preserving Smart Home Architecture Implementing Federated Learning},
  author    = {A{\"i}vodji, UM and Gambs, S and Martin, A},
  booktitle = {2019 IEEE Security and Privacy Workshops (SPW)},
  year      = {2019}
}

@inproceedings{silva2019federated,
  title     = {Federated Learning in Distributed Medical Databases: Meta-analysis of Large-Scale Subcortical Brain Data},
  author    = {Silva, S and Gutman, BA and Romero, E and others},
  booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
  year      = {2019}
}

@article{gao2019hhhfl,
  title   = {HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography},
  author  = {Gao, D and Ju, C and Wei, X and others},
  journal = {arXiv preprint arXiv:1909.05784},
  year    = {2019}
}

@inproceedings{kim2007federated,
  title     = {Federated Tensor Factorization for Computational Phenotyping},
  author    = {Kim, Y and Sun, J and Yu, H and others},
  booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  publisher = {Association for Computing Machinery},
  address   = {Halifax, NS, Canada},
  pages     = {887--895},
  year      = {2007}
}

@article{pfohl2019federated,
  title   = {Federated and Differentially Private Learning for Electronic Health Records},
  author  = {Pfohl, SR and Dai, AM and Heller, KA},
  journal = {arXiv preprint arXiv:1911.05861},
  year    = {2019}
}

@article{brisimi2018federated,
  title   = {Federated Learning of Predictive Models from Federated Electronic Health Records},
  author  = {Brisimi, TS and Chen, R and Mela, T and others},
  journal = {International Journal of Medical Informatics},
  volume  = {112},
  year    = {2018}
}

@article{huang2019patient,
  title   = {Patient Clustering Improves Efficiency of Federated Machine Learning to Predict Mortality and Hospital Stay Time Using Distributed Electronic Medical Records},
  author  = {Huang, L and Shea, AL and Qian, H and others},
  journal = {Journal of Biomedical Informatics},
  volume  = {99},
  year    = {2019}
}

@article{huang2020loadaboost,
  title   = {Loadaboost: Loss-based Adaboost Federated Machine Learning with Reduced Computational Complexity on Iid and Non-IID Intensive Care Data},
  author  = {Huang, L and Yin, Y and Fu, Z and others},
  journal = {PLOS ONE},
  volume  = {2020},
  number  = {4}
}

@article{chen2021lianhe,
  title   = {联邦学习在金融行业的应用分析},
  author  = {陈琨 and 李艺 and 王国赛},
  journal = {征信},
  volume  = {39},
  number  = {10},
  pages   = {29-36},
  year    = {2021}
}


% 以下类型未在bst文件中验证过，如果需要请自行修改bst文件……

% (3)论文集
% [序号] 作者.论文题目.见(英文用In).主编.论文集名.出版地.出版年:页码范围.
% 本模板没有实现“主编”部分，因为“见”和“论文集名”分离实在是很诡异的事情，且很多事情我们得到的bibTeX不含主编
% 中文需要加language={Chinese}字段，才会把"In"换成"见"


% (4)学位论文（格式已验证）
% [序号] 作者.题目.[学位论文](英文用[Dissertation]).保存地点.保存单位:年份.
% 中文需要使用language={Chinese}字段，英文输入language={}



% (5)专利
% [序号] 专利申请者.题目.国别.专利文献种类.专利号.批准日期.

% (6)技术标准
% [序号] 起草责任者.标准代号.标准顺序号－发布年.标准名称.出版地.出版者.出版年度.


%(7)在线资料

@misc{lendingclubdata,
  title        = {Lending Club Data},
  url          = {https://www.lendingclub.com/info/download-data.action},
  howpublished = {online}
}

@misc{fate,
  title        = {FATE开源文档},
  url          = {https://github.com/FederatedAI/FATE},
  howpublished = {online}
}

%(8)不属于任何上述类别的资料，指导手册中无规定，使用默认设置
@misc{联邦学习白皮书,
  author = {微众银行AI项目组},
  title  = {联邦学习白皮书 V1.0[R]},
  year   = {2018}
}