Feng Liang, Enyue Yang, Weike Pan, Qiang Yang and Zhong Ming. A Survey of Recommender Systems Based on Federated Learning (in Chinese) [J]. SCIENTIA SINICA Informationis, 52(5):1-29, 2022.
去中心化的分布式矩阵分解框架(DMF):Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li. Privacy preserving point-of-interest recommendation using decentralized matrix factorization [C]. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), pages 257-264, 2018. paper
联邦协同过滤推荐算法(FCF):Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, Adrian Flanagan. Federated collaborative filtering for privacy-preserving personalized recommendation system [J]. CoRR, 2019, abs/1901.09888. Paper
基于隐私保护的余弦相似度算法(PPCSC):Harmanjeet Kaur, Neeraj Kumar, Mohammad S. Obaidat. Multi-party secure collaborative filtering for recommendation generation [C]. In: Proceedings of 2019 IEEE Global Communications Conference (GLOBECOM'19), pages 1-6, 2019. paper
用户能调节自身隐私级别的去中心化分布式矩阵分解框架(PDMFRec):Erika Duriakova, Elias Z. Tragos, Barry Smyth, Neil Hurley, Francisco J. Pena, Panagiotis Symeonidis, James Geraci, and Aonghus Lawlor. PDMFRec: A decentralised matrix factorisation with tunable user-centric privacy [C]. In: Proceedings of the 13th ACM Conference on Recommender Systems (RecSys'19), pages 457-461, 2019. paper
联邦矩阵分解算法(FederatedMF):Koustabh Dolui, Illapha Cuba Gyllensten, Dietwig Lowet, Sam Michiels, Hans Hallez, Danny Hughes. Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization [C]. In: Proceedings of the 17th Annual International Conference on Mobile Systems, applications, and Services (MobiSys'19), pages 624--625, 2019. paper
基于秘密共享技术的共享矩阵分解方法(SharedMF):Senci Ying. Shared MF: a privacy-preserving recommendation system [J]. CoRR, 2020, abs/2008.07759. paper
基于FATE平台的在线联邦推荐系统(FedRecSys):Ben Tan, Bo Liu, Vincent W. Zheng, Qiang Yang. A federated recommender system for online services [C]. In: Proceedings of 14th ACM Conference on Recommender Systems (RecSys'20), pages 579-581, 2020. paper
基于位置敏感哈希的联邦推荐算法(FRecLSH):Hongsheng Hu, Gillian Dobbie, Zoran Salcic, Meng Liu, Jianbing Zhang, Xuyun Zhang. A locality sensitive hashing based approach for federated recommender system [C]. In: Proceedings of 20th International Symposium on Cluster, Cloud and Internet Computing (CCGRID'20), pages 836-842, 2020. paper
基于差分隐私技术的本地协同过滤算法(DPLCF):Chen Gao, Chao Huang, Dongsheng Lin, Depeng Jin, Yong Li. DPLCF: differentially private local collaborative filtering [C]. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'20), pages 961–970, 2020. paper
安全社交推荐框架(SeSoRec):Chaochao Chen, Liang Li, Bingzhe Wu, Cheng Hong, Li Wang, Jun Zhou. Secure social recommendation based on secret sharing [C]. In: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI'20), pages 506-512, 2020. paper
可快速训练的联邦推荐框架(FedFast):Khalil Muhammad, Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Neil Hurley , James Geraci, Aonghus Lawlor. FedFast: going beyond average for faster training of federated recommender systems [C]. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2020), pages 1234–1242, 2020. paper
基于深度结构化语义模型的联邦多视图框架(FL-MV-DSSM):Mingkai Huang, Hao Li, Bing Bai, Chang Wang, Kun Bai, Fei Wang. A federated multi-view deep learning framework for privacy-preserving recommendations [J]. CoRR, 2020, abs/2008.10808. paper
安全的联邦子模型学习框架(SFSL):Chaoyue Niu, Fan Wu, Shaojie Tang, Lifeng Hua, Rongfei Jia, Chengfei Lv, Zhihua Wu, Guihai Chen. Billion-scale federated learning on mobile clients: A submodel design with tunable privacy [C]. In: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (MobiCom'20), pages 31:1-31:14, 2020. paper
联邦多视图矩阵分解算法(FED-MVMF):Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A. Khan, Muhammad Ammad-ud-din. Federated multi-view matrix factorization for personalized recommendations [J]. CoRR, 2020, abs/2004.04256. paper
基于联邦学习的元矩阵分解框架(MetaMF):Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, Xiuzhen Cheng. Meta Matrix Factorization for Federated Rating Predictions [C]. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval (SIGIR'20), pages 981-990, 2020. paper
隐私保护的推荐框架(PriRec):Chaochao Chen, Jun Zhou, Bingzhe Wu, Wenjing Fang, Li Wang, Yuan Qi, Xiaolin Zheng. Practical privacy preserving POI recommendation [J]. ACM Transactions on Intelligent Systems and Technology, 2020, 11(5):52:1-52:20. paper
联邦新闻推荐框架(FedNewsRec):Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie. Privacy-preserving news recommendation model training via federated learning [J]. CoRR, 2020, abs/2003.09592. paper
面向显式反馈的无损联邦推荐算法(FedRec++):Feng Liang, Weike Pan, Zhong Ming. FedRec++: Lossless federated recommendation with explicit feedback [C]. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'21), pages 4224-4231, 2021. paper
基于同态加密技术的安全联邦矩阵分解框架(FedMF):Di Chai, Leye Wang, Kai Chen, Qiang Yang. Secure federated matrix factorization [J]. IEEE Intelligent Systems, 2021, 36(5):11-20. paper
隐私保护的推荐系统框架(PPRSF):Jiangcheng Qin, Baisong Liu, Jiangbo Qian. A novel privacy-preserved recommender system framework based on federated learning [C]. In: Proceedings of the 4th International Conference on Software Engineering and Information Management (ICSIM'2021), pages 82-88, 2021. paper
强隐私保护的面向隐式反馈的联邦协同过滤:Lorenzo Minto, Moritz Haller, Benjamin Livshits, Hamed Haddadi. Stronger privacy for federated collaborative filtering with implicit feedback [C]. In: Proceedings of the 15th ACM Conference on Recommender Systems (RecSys'21), pages 342–350, 2021. paper
联邦序列推荐模型(DeepRec):Jialiang Han, Yun Ma, Qiaozhu Mei, Xuanzhe Liu. DeepRec: On-device deep learning for privacy-preserving sequential recommendation in mobile commerce [C]. In: Proceedings of the 30th International Conference on World Wide Web (WWW'21), pages 900–911, 2021. paper
GNN联邦推荐学习框架(FedGNN):Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, Xing Xie. FedGNN: federated graph neural network for privacy-preserving recommendation [J]. CoRR, 2021, abs/2102.04925. paper
基于虚假掩码和秘密共享的联邦推荐(FR-FMSS):Zhaohao Lin, Weike Pan, Zhong Ming. FR-FMSS: federated recommendation via fake marks and secret sharing [C]. In: Proceedings of the 15th ACM Conference on Recommender Systems (RecSys'21), pages 668–673, 2021. paper
% Journal
@article{Journal-CN-SSI-22-Suvery,
author = {梁锋,羊恩跃,潘微科,杨强,明仲},
title = {基于联邦学习的推荐系统综述},
journal = {中国科学:信息科学},
volume = {52},
number = {5},
pages = {1--29},
year = {2022}
}
英文:
代码语言:javascript
复制
% Journal
@article{Journal-CN-SSI-22-Suvery,
author = {Feng Liang and Enyue Yang and Weike Pan and Qiang Yang and Zhong Ming},
title = {A Survey of Recommender Systems Based on Federated Learning (in Chinese)},
journal = {SCIENTIA SINICA Informationis},
volume = {52},
number = {5},
pages = {1--29},
year = {2022}
}
联邦学习(Federated Learning,FELE)是一种打破数据孤岛、释放 AI 应用潜能的分布式机器学习技术,能够让联邦学习各参与方在不披露底层数据和底层数据加密(混淆)形态的前提下,通过交换加密的机器学习中间结果实现联合建模。该产品兼顾AI应用与隐私保护,开放合作,协同性高,充分释放大数据生产力,广泛适用于金融、消费互联网等行业的业务创新场景。