Survey on federated recommendation systems

Zhitao ZHU; Shijing SI; Jianzong WANG; Jing XIAO

doi:10.11959/j.issn.2096-0271.2022032

您当前的位置：

首页 >

文章列表页 >

Survey on federated recommendation systems

STUDY | 更新时间：2024-06-03

- Survey on federated recommendation systems
- Big data research Vol. 8, Issue 4, Pages: 105-132(2022)
- 作者机构：
  
  1. 平安科技（深圳）有限公司，广东深圳 518063
  2. 中国科学技术大学，安徽合肥 230026
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2022032
  CLC： TP391
- Online First：2022-07，
  
  Published：15 July 2022
- 稿件说明：
移动端阅览
Zhitao ZHU, Shijing SI, Jianzong WANG, et al. Survey on federated recommendation systems[J]. Big data research, 2022, 8(4): 105-132.
DOI：

Zhitao ZHU, Shijing SI, Jianzong WANG, et al. Survey on federated recommendation systems[J]. Big data research, 2022, 8(4): 105-132. DOI： 10.11959/j.issn.2096-0271.2022032.

摘要

在联邦学习范式中，原始数据被本地存储在独立的用户客户端中，而脱敏数据被发送到中心服务器中加以聚合，这给众多领域提供了一种新颖的设计思路。考虑到传统推荐系统的研究方向集中于提高推荐效果，在资源节约、跨领域推荐、隐私保护等方面还具有很大改进空间，如何将联邦学习与推荐系统结合以解决这些问题成为当前的一个研究热点。对近年来基于联邦学习的推荐系统进行了全面的总结、比较与分析，首先介绍了推荐系统的传统实现方式及面临的瓶颈；然后引入了联邦学习范式，描述了联邦学习在隐私保护、利用多领域用户数据两方面给推荐系统带来的增益，以及二者结合的技术挑战，进而详细说明了现有的联邦推荐系统部署方式；最后，对联邦推荐系统未来的研究进行了展望与总结。

Abstract

In the federated learning (FL) paradigm

the original data are stored in independent clients while masked data are sent to a central server to be aggregated

which proposes a novel design approach to numerous domains.Given the wide application of recommendation systems (RS) in diverse domains

combining RS with FL techniques has been gaining momentum to reduce the computational cost

do cross-domain recommendation and protect users’ privacy while maintaining recommendations performance as traditional RS.The federated learning-based recommendation systems in recent years were comprehensively summarized.The difference between traditional and federated recommendation systems was analyzed

and the main research direction and progress of federated recommendation systems were demonstrated with comparison and analysis.Firstly

the traditional recommendation systems and their bottleneck were summarized.Then the federated learning paradigm was introduced.Furthermore

the advantages of combining federated learning with recommendation systems were depicted in two aspects: privacy protection and usage of multi-domain user information

along with the technical challenges during the combination.At the same time

the existing deployment of federated recommendation systems was illustrated in detail.Finally

future research on federated recommendation systems was prospected and summarized.

关键词

Keywords

references

ZHANG S , YAO L N , SUN A X , et al . Deep learning based recommender system [J ] . ACM Computing Surveys , 2020 , 52 ( 1 ): 1 - 38 .

RICCI F , ROKACH L , SHAPIRA B . Introduction to recommender systems handbook [M ] // Recommender Systems Handbook . Heidelberg : Springer , 2011 .

ALBRECHT J P . How the GDPR will change the world [J ] . European Data Protection Law Review , 2016 , 2 ( 3 ): 287 - 289 .

MOONEY R J , ROY L . Content-based book recommending using learning for text categorization [C ] // Proceedings of the 5th ACM Conference on Digital Libraries . New York:ACM Press , 2000 : 195 - 204 .

SHARDANAND U , MAES P . Social information filtering:algorithms for automating “word of mouth” [C ] // Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . New York:ACM Press , 1995 : 210 - 217 .

MILLER B N , ALBERT I , LAM S K , et al . MovieLens unplugged:experiences with an occasionally connected recommender system [C ] // Proceedings of the 8th International Conference on Intelligent User Interfaces . New York:ACM Press , 2003 : 263 - 266 .

TERVEEN L , HILL W , AMENTO B , et al . Phoaks:a system for sharing recommendations [J ] . Communications of the ACM , 1997 , 40 ( 3 ): 59 - 62 .

GOLDBERG K , ROEDER T , GUPTA D , et al . Eigentaste:a constant time collaborative filtering algorithm [J ] . Information Retrieval , 2001 , 4 ( 2 ): 133 - 151 .

BILLSUS D , PAZZANI M J . Learning collaborative information filters [C ] // Proceedings of the 15th International Conference on Machine Learning.San Francisco:Morgan Kaufmann Publishers Inc . , 1998 : 46 - 54 .

ADOMAVICIUS G , TUZHILIN A . Toward the next generation of recommender systems:a survey of the state-of-theart and possible extensions [J ] . IEEE Transactions on Knowledge and Data Engineering , 2005 , 17 ( 6 ): 734 - 749 .

DESHPANDE M , KARYPIS G . Item-based top-N recommendation algorithms [J ] . ACM Transactions on Information Systems , 2004 , 22 ( 1 ): 143 - 177 .

SU X Y , KHOSHGOFTAAR T M . A survey of collaborative filtering techniques [J ] . Advances in Artificial Intelligence , 2009 :421425.

CHOI K , YOO D , KIM G , et al . A hybrid online-product recommendation system:combining implicit rating-based collaborative filtering and sequential pattern analysis [J ] . Electronic Commerce Research and Applications , 2012 , 11 ( 4 ): 309 - 317 .

XIANG L , YUAN Q , ZHAO S W , et al . Temporal recommendation on graphs via long- and short-term preference fusion [C ] // Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2010 : 723 - 732 .

BREESE J S , HECKERMAN D , KADIE C . Empirical analysis of predictive algorithms for collaborative filtering [C ] // Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.San Francisco:Morgan Kaufmann Publishers Inc . , 1998 : 43 - 52 .

UNGAR L H , FOSTER D P . Clustering methods for collaborative filtering [C ] // Proceedings of 1998 AAAI Workshop on Recommendation Systems . Palo Alto:AAAI Press , 1998 : 114 - 129 .

黄立威 , 江碧涛 , 吕守业 , 等 . 基于深度学习的推荐系统研究综述 [J ] . 计算机学报 , 2018 , 41 ( 7 ): 1619 - 1647 .

HUANG L W , JIANG B T , LYU S Y , et al . Survey on deep learning based recommender systems [J ] . Chinese Journal of Computers , 2018 , 41 ( 7 ): 1619 - 1647 .

PAZZANI M J , BILLSUS D . Contentbased recommendation systems [M ] // The adaptive Web . Heidelberg : Springer , 2007 .

SALTON G , WONG A , YANG C S . A vector space model for automatic indexing [J ] . Communications of the ACM , 1975 , 18 ( 11 ): 613 - 620 .

BURKE R . Hybrid recommender systems:survey and experiments [J ] . User Modeling and User-Adapted Interaction , 2002 , 12 ( 4 ): 331 - 370 .

WANG H , WANG N Y , YEUNG D Y . Collaborative deep learning for recommender systems [C ] // Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2015 : 1235 - 1244 .

YANG X W , STECK H , LIU Y . Circlebased recommendation in online social networks [C ] // Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2012 : 1267 - 1275 .

KRULWICH B . LIFESTYLE FINDER:intelligent user profiling using large-scale demographic data [J ] . AI Magazine , 1997 , 18 ( 2 ): 37 - 45 .

GONZÁLEZ G , LOPEZ B , ROSA J L . A multi-agent smart user model for cross-domain recommender systems [C ] // Proceedings of 2005 International Conference on Intelligent User Interfaces:Beyond Personalization . New York:ACM Press , 2005 .

DENG L . Deep learning:methods and applications [J ] . Foundations and Trends in Signal Processing , 2014 , 7 ( 3/4 ): 197 - 387 .

王健宗 , 黄章成 , 肖京 . 人工智能赋能金融科技 [J ] . 大数据 , 2018 , 4 ( 3 ): 111 - 116 .

WANG J Z , HUANG Z C , XIAO J . Artificial intelligence energize Fintech [J ] . Big Data Research , 2018 , 4 ( 3 ): 111 - 116 .

SALAKHUTDINOV R , MNIH A , HINTON G . Restricted Boltzmann machines for collaborative filtering [C ] // Proceedings of the 24th International Conference on Machine Learning . New York:ACM Press , 2007 : 791 - 798 .

SEDHAIN S , MENON A K , SANNER S , et al . AutoRec:autoencoders meet collaborative filtering [C ] // Proceedings of the 24th International Conference on World Wide Web . New York:ACM Press , 2015 : 111 - 112 .

HE X N , LIAO L Z , ZHANG H W , et al . Neural collaborative filtering [C ] // Proceedings of the 26th International Conference on World Wide Web . New York:ACM Press , 2017 : 173 - 182 .

ZHOU G R , ZHU X Q , SONG C R , et al . Deep interest network for click-through rate prediction [C ] // Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . New York:ACM Press , 2018 : 1059 - 1068 .

ZHOU G R , MOU N , FAN Y , et al . Deep interest evolution network for clickthrough rate prediction [C ] // Proceedings of 2019 AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2019 : 5941 - 5948 .

LI C , LIU Z Y , WU M M , et al . Multiinterest network with dynamic routing for recommendation at Tmall [C ] // Proceedings of the 28th ACM International Conference on Information and Knowledge Management . New York:ACM Press , 2019 : 2615 - 2623 .

HE X N , CHUA T S . Neural factorization machines for sparse predictive analytics [C ] // Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2017 : 355 - 364 .

GUO H F , TANG R M , YE Y M , et al . DeepFM:a factorization-machine based neural network for CTR prediction [C ] // Proceedings of the 26th International Joint Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2017 : 1725 - 1731 .

XIAO J , YE H , HE X N , et al . Attentional factorization machines:learning the weight of feature interactions via attention networks [C ] // Proceedings of the 26th International Joint Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2017 : 3119 - 3125 .

GONG Y Y , ZHANG Q . Hashtag recommendation using attentionbased convolutional neural network [C ] // Proceedings of the 25th International Joint Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2016 : 2782 - 2788 .

ZHANG Q , WANG J W , HUANG H R , et al . Hashtag recommendation for multimodal microblog using coattention network [C ] // Proceedings of the 26th International Joint Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2017 : 3420 - 3426 .

OORD A V D , DIELEMAN S , SCHRAUWEN B . Deep content-based music recommendation [C ] // Proceedings of the 26th International Conference on Nerual Information Processing Systems.Red Hook:Curran Associates Inc . , 2013 : 2643 - 2651 .

LEI C Y , LIU D , LI W P , et al . Comparative deep learning of hybrid representations for image recommendations [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 2545 - 2553 .

HIDASI B A Z , KARATZOGLOU A , BALTRUNAS L , et al . Session-based recommendations with recurrent neural networks [C ] // Proceedings of 2015 International Conference on Learning Representations .[S.l.:s.n. ] , 2015 .

LIU Q , WU S , WANG L . Multi-behavioral sequential prediction with recurrent logbilinear model [J ] . IEEE Transactions on Knowledge and Data Engineering , 2017 , 29 ( 6 ): 1254 - 1267 .

WU C H , WANG J W , LIU J T , et al . Recurrent neural network based recommendation for time heterogeneous feedback [J ] . Knowledge-Based Systems , 2016 , 109 : 90 - 103 .

LI Y , LIU T , JIANG J , et al . Hashtag recommendation with topical attentionbased LSTM [C ] // Proceedings of the 26th International Conference on Computational Linguistics . Cambridge:The MIT Press , 2016 : 943 - 952 .

MCMAHAN B , MOORE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [C ] // Proceedings of the 20th International Conference on Artificial Intelligence and Statistics .[S.l.:s.n. ] , 2017 : 1273 - 1282 .

KONEČNÝ J , MCMAHAN B , RAMAGE D . Federated optimization:Distributed optimization beyond the datacenter [J ] . arXiv preprint , 2015 ,arXiv:151103575.

KAIROUZ E B P , MCMAHAN H B . Advances and open problems in federated learning [J ] . Foundations and Trends in Machine Learning , 2021 , 14 ( 1 ).

LI Q B , WEN Z Y , WU Z M , et al . Federated learning systems:vision,hype and reality for data privacy and protection [J ] . arXiv preprint , 2019 ,arXiv:190709693.

HARD A , RAO K , MATHEWS R , et al . Federated learning for mobile keyboard prediction [J ] . arXiv preprint , 2018 ,arXiv:181103604.

YANG Q , LIU Y , CHEN T J , et al . Federated machine learning [J ] . ACM Transactions on Intelligent Systems and Technology , 2019 , 10 ( 2 ): 1 - 19 .

PAN S J , YANG Q . A survey on transfer learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2010 , 22 ( 10 ): 1345 - 1359 .

吴建汉 , 司世景 , 王健宗 , 等 . 联邦学习攻击与防御综述 [J ] . 大数据 , 2022 :2022038.

WU J H , SI S J , WANG J Z , et al . Threats and defenses of federated learning:a survey [J ] . Big Data Research , 2022 :2022038.

周俊 , 董晓蕾 , 曹珍富 . 推荐系统的隐私保护研究进展 [J ] . 计算机研究与发展 , 2019 , 56 ( 10 ): 2033 - 2048 .

ZHOU J , DONG X L , CAO Z F . Research advances on privacy preserving in recommender systems [J ] . Journal of Computer Research and Development , 2019 , 56 ( 10 ): 2033 - 2048 .

KIM S , KIM J , KOO D , et al . Efficient privacy-preserving matrix factorization via fully homomorphic encryption:extended abstract [C ] // Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security . New York:ACM Press , 2016 : 617 - 628 .

AGRAWAL R , SRIKANT R . Privacypreserving data mining [C ] // Proceedings of 2000 ACM SIGMOD International Conference on Management of Data . New York:ACM Press , 2000 : 439 - 50 .

POLAT H , DU W L . Privacy-preserving collaborative filtering using randomized perturbation techniques [C ] // Proceedings of 3rd IEEE International Conference on Data Mining . Piscataway:IEEE Press , 2003 : 625 - 628 .

HERLOCKER J , KONSTAN J , BORCHERS A , et al . An algorithmic framework for performing collaborative filtering [C ] // Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 1999 : 230 - 237 .

CHEN K K , LIU L . Privacy preserving data classification with rotation perturbation [C ] // Proceedings of 5th IEEE International Conference on Data Mining . Piscataway:IEEE Press , 2005 : 589 - 592 .

DWORK C . A firm foundation for private data analysis [J ] . Communications of the ACM , 2011 , 54 ( 1 ): 86 - 95 .

DWORK C , ROTH A . The algorithmic foundations of differential privacy [J ] . Foundations and Trends in Theoretical Computer Science , 2013 , 9 ( 3/4 ): 211 - 407 .

MCSHERRY F , MIRONOV I . Differentially private recommender systems:building privacy into the Netflix prize contenders [C ] // Proceedings of the 15th ACM SIGKDD International Conference on Knowledge discovery and data mining . New York:ACM Press , 2009 : 627 - 636 .

ZHU T Q , REN Y L , ZHOU W L , et al . An effective privacy preserving algorithm for neighborhood-based collaborative filtering [J ] . Future Generation Computer Systems , 2014 , 36 : 142 - 155 .

BERLIOZ A , FRIEDMAN A , KAAFAR M A , et al . Applying differential privacy to matrix factorization [C ] // Proceedings of the 9th ACM Conference on Recommender Systems . New York:ACM Press , 2015 : 107 - 114 .

OREKONDY T , OH S J , ZHANG Y , et al . Gradient-leaks:understanding and controlling deanonymization in federated learning [J ] . arXiv preprint , 2018 ,arXiv:180505838.

WANG Z B , SONG M K , ZHANG Z F , et al . Beyond inferring class representatives:user-level privacy leakage from federated learning [C ] // Proceedings of 2019 IEEE Conference on Computer Communications . Piscataway:IEEE Press , 2019 : 2512 - 2520 .

MELIS L , SONG C Z , DE CRISTOFARO E , et al . Exploiting unintended feature leakage in collaborative learning [C ] // Proceedings of 2019 IEEE Symposium on Security and Privacy . Piscataway:IEEE Press , 2019 : 691 - 706 .

王健宗 , 孔令炜 , 黄章成 , 等 . 联邦学习隐私保护研究进展 [J ] . 大数据 , 2021 , 7 ( 3 ): 130 - 149 .

WANG J Z , KONG L W , HUANG Z C , et al . Research advances on privacy protection of federated learning [J ] . Big Data Research , 2021 , 7 ( 3 ): 130 - 149 .

周传鑫 , 孙奕 , 汪德刚 , 等 . 联邦学习研究综述 [J ] . 网络与信息安全学报 , 2021 , 7 ( 5 ): 77 - 92 .

ZHOU C X , SUN Y , WANG D G , et al . Survey of federated learning research [J ] . Chinese Journal of Network and Information Security , 2021 , 7 ( 5 ): 77 - 92 .

TRUEX S , LIU L , CHOW K H , et al . LDP-Fed:federated learning with local differential privacy [C ] // Proceedings of the 3rd ACM International Workshop on Edge Systems,Analytics and Networking . New York:ACM Press , 2020 : 61 - 66 .

LIU R X , CAO Y , YOSHIKAWA M , et al . FedSel:federated SGD under local differential privacy with top-k dimension selection [C ] // Proceedings of the International Conference on Database Systems for Advanced Applications . Heidelberg:Springer , 2020 : 485 - 501 .

TRIASTCYN A , FALTINGS B . Federated learning with Bayesian differential privacy [C ] // Proceedings of 2019 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2019 : 2587 - 2596 .

BONAWITZ K , IVANOV V , KREUTER B , et al . Practical secure aggregation for privacy-preserving machine learning [C ] // Proceedings of 2017 ACM SIGSAC Conference on Computer and Communications Security . New York:ACM Press , 2017 : 1175 - 1191 .

LI T , SONG L Q , FRAGOULI C . Federated recommendation system via differential privacy [C ] // Proceedings of 2020 IEEE International Symposium on Information Theory . Piscataway:IEEE Press , 2020 : 2592 - 2597 .

LIU S C , XU S Y , YU W H , et al . FedCT:federated collaborative transfer for recommendation [C ] // Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2021 : 716 - 725 .

王健宗 , 肖京 , 朱星华 , 等 . 联邦推荐系统的协同过滤冷启动解决方法 [J ] . 智能系统学报 , 2021 , 16 ( 1 ): 178 - 185 .

WANG J Z , XIAO J , ZHU X H , et al . Cold starts in collaborative filtering for federated recommender systems [J ] . CAAI Transactions on Intelligent Systems , 2021 , 16 ( 1 ): 178 - 185 .

WANG L , WANG Y H , BAI Y , et al . POI recommendation with federated learning and privacy preserving in cross domain recommendation [C ] // Proceedings of 2021 IEEE Conference on Computer Communications Workshops . Piscataway:IEEE Press , 2021 : 1 - 6 .

ZONG L L , XIE Q J , ZHOU J H , et al . FedCMR:federated cross-modal retrieval [C ] // Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2021 : 1672 - 1676 .

MA J , ZHANG Q C , LOU J , et al . Communication efficient federated generalized tensor factorization for collaborative health data analytics [C ] // Proceedings of the International WorldWide Web Conference . New York:ACM Press , 2021 : 171 - 182 .

CAO L B . Non-IID recommender systems:a review and framework of recommendation paradigm shifting [J ] . Engineering , 2016 , 2 ( 2 ): 212 - 224 .

WANG C , CAO L B , WANG M C , et al . Coupled nominal similarity in unsupervised learning [C ] // Proceedings of the 20th ACM international conference on Information and knowledge management . New York:ACM Press , 2011 : 973 - 978 .

WANG C , DONG X J , ZHOU F , et al . Coupled attribute similarity learning on categorical data [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2015 , 26 ( 4 ): 781 - 797 .

王健宗 , 孔令炜 , 黄章成 , 等 . 联邦学习算法综述 [J ] . 大数据 , 2020 , 6 ( 6 ): 64 - 82 .

WANG J Z , KONG L W , HUANG Z C , et al . Research review of federated learning algorithms [J ] . Big Data Research , 2020 , 6 ( 6 ): 64 - 82 .

WU Q , HE K , CHEN X . Personalized federated learning for intelligent IoT applications:a cloud-edge based framework [J ] . IEEE Computer Graphics and Applications , 2020 , 1 : 35 - 44 .

YANG C X , WANG Q P , XU M W , et al . Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data [C ] // Proceedings of 2021 International World-Wide Web Conference. . New York:ACM Press , 2021 : 935 - 946 .

LIU L M , ZHANG J , SONG S H , et al . Clientedge-cloud hierarchical federated learning [C ] // Proceedings of 2020 IEEE International Conference on Communications . Piscataway:IEEE Press , 2020 : 1 - 6 .

XIE C , KOYEJO S , GUPTA I . Asynchronous federated optimization [J ] . arXiv preprint,2019,arXiv:1903.03934 .

LI X , HUANG K X , YANG W H , et al . On the convergence of FedAvg on NonIID data [C ] // Proceedings of the 7th International Conference on Learning Representations .[S.l.:s.n. ] , 2019 .

ZHAO Y , LI M , LAI L Z , et al . Federated learning with Non-IID data [J ] . arXiv preprint , 2018 ,arXiv:180600582.

KULKARNI V , KULKARNI M , PANT A . Survey of personalization techniques for federated learning [C ] // Proceedings of 2020 4th World Conference on Smart Trends in Systems,Security and Sustainability . Piscataway:IEEE Press , 2020 : 794 - 797 .

WU J Z , LIU Q , HUANG Z Y , et al . Hierarchical personalized federated learning for user modeling [C ] // Proceedings of 2021 Web Conference . New York:ACM Press , 2021 : 957 - 968 .

ROTHCHILD D , PANDA A , ULLAH E , et al . FetchSGD:communication-efficient federated learning with sketching [C ] // Proceedings of the 37th International Conference on Machine Learning . New York:ACM Press , 2020 .

REISIZADEH A , MOKHTARI A , HASSANI H , et al . FedPAQ:a communication-efficient federated learning method with periodic averaging and quantization [C ] // Proceedings of 2020 International Conference on Artificial Intelligence and Statistics .[S.l.:s.n. ] , 2020 : 2021 - 2031 .

KHAN F K , FLANAGAN A , TAN K E , et al . A payload optimization method for federated recommender systems [C ] // Proceedings of the 15th ACM Conference on Recommender Systems . New York:ACM Press , 2021 : 432 - 442 .

MALINOVSKY G , KOVALEV D , GASANOV E , et al . From local SGD to local fixed point methods for federated learning [C ] // Proceedings of the 37th International Conference on Machine Learning . New York:ACM Press , 2020 : 6692 - 6701 .

KONEČNÝ J , MCMAHAN H B , YU F X , et al . Federated learning:strategies for improving communication efficiency [C ] // Proceedings of the NIPS Workshop on Private Multi-Party Machine Learning . Cambridge:The MIT Press , 2016 .

MANSOUR Y , MOHRI M , RO J , et al . Three approaches for personalization with applications to federated learning [J ] . arXiv preprint,2020,arXiv:2002.10619 .

SMITH V , CHIANG C K , SANJABI M , et al . Federated multi-task learning [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems.Red Hook:Curran Associates Inc . , 2017 : 4427 - 4437 .

LINDEN G , SMITH B , YORK J.Amazon . com recommendations:item-to-item collaborative filtering [J ] . IEEE Internet Computing , 2003 , 7 ( 1 ): 76 - 80 .

LI D L , WANG J P . FedMD:heterogenous federated learning via model distillation [J ] . arXiv preprint,2019,arXiv:1910.03581 .

ARIVAZHAGAN M G , AGGARWAL V , SINGH A K , et al . Federated learning with personalization layers [J ] . arXiv preprint,2019,arXiv:1912.00818 .

HANZELY F , RICHTÁRIK P , . Federated learning of a mixture of global and local models [J ] . arXiv preprint,2020,arXiv:2002.05516 .

WANG J Y , JOSHI G . Cooperative SGD:a unified framework for the design and analysis of communication-efficient SGD algorithms [C ] // Proceedings of the ICML Workshop on Coding Theory for Machine Learning .[S.l.:s.n. ] , 2019 .

LI X , YANG W H , WANG S S , et al . Communication efficient decentralized training with multiple local updates [J ] . arXiv preprint,2019,arXiv:1910.09126 .

LIANG P P , LIU T , ZIYIN L , et al . Think locally,act globally:federated learning with local and global representations [J ] . arXiv preprint , 2020 ,arXiv:200101523.

LIU Y , KANG Y , ZHANG X W , et al . A communication efficient vertical federated learning framework [J ] . arXiv preprint,2019,arXiv:1912.11187 .

马嘉华 , 孙兴华 , 夏文超 , 等 . 基于标签量信息的联邦学习节点选择算法 [J ] . 物联网学报 , 2021 , 5 ( 4 ): 46 - 53 .

MA J H , SUN X H , XIA W C , et al . Node selection based on label quantity information in federated learning [J ] . Chinese Journal on Internet of Things , 2021 , 5 ( 4 ): 46 - 53 .

AMMAD-UD-DIN M , IVANNIKOVA E , KHAN S A , et al . Federated collaborative filtering for privacy-preserving personalized recommendation system [J ] . arXiv preprint , 2019 ,arXiv:190109888.

CHAI D , WANG L Y , CHEN K , et al . Secure federated matrix factorization [J ] . IEEE Intelligent Systems , 2021 , 36 ( 5 ): 11 - 20 .

MINTO L , HALLER M , LIVSHITS B , et al . Stronger privacy for federated collaborative filtering with implicit feedback [C ] // Proceedings of the 15th ACM Conference on Recommender Systems . New York:ACM Press , 2021 : 342 - 350 .

LIN G Y , LIANG F , PAN W K , et al . FedRec:federated recommendation with explicit feedback [J ] . IEEE Intelligent Systems , 2021 , 36 ( 5 ): 21 - 30 .

LIANG F , PAN W , MING Z . FedRec++:lossless federated recommendation with explicit feedback [C ] // Proceedings of the 35th AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2021 : 4224 - 4231 .

QI T , WU F Z , WU C H , et al . Privacypreserving news recommendation model learning [C ] // Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing:Findings .[S.l. ] : ACL Press , 2020 : 1423 - 32 .

OKURA S , TAGAMI Y , ONO S , et al . Embedding-based news recommendation for millions of users [C ] // Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2017 : 1933 - 1942 .

HUANG M K , LI H , BAI B , et al . A federated multi-view deep learning framework for privacy-preserving recommendations [J ] . arXiv preprint,2020,arXiv:2008.10808 .

HUANG P S , HE X D , GAO J F , et al . Learning deep structured semantic models for web search using clickthrough data [C ] // Proceedings of the 22nd ACM International Conference on Information ＆Knowledge Management . New York:ACM Press , 2013 : 2333 - 2338 .

LIN Y J , REN P J , CHEN Z M , et al . Meta matrix factorization for federated rating predictions [C ] // Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2020 : 981 - 990 .

MUHAMMAD K , WANG Q Q , O’REILLYMORGAN D , et al . FedFast:going beyond average for faster training of federated recommender systems [C ] // Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery ＆Data Mining . New York:ACM Press , 2020 : 1234 - 1242 .

KHARITONOV E , . Federated online learning to rank with evolution strategies [C ] // Proceedings of the 12th ACM International Conference on Web Search and Data Mining . New York:ACM Press , 2019 : 249 - 257 .

TRIENES J , CANO A T , HIEMSTRA D . Recommending users:whom to follow on federated social networks [J ] . arXiv preprint , 2018 ,arXiv:181109292.

TAN B , LIU B , ZHENG V , et al . A federated recommender system for online services [C ] // Proceedings of the 14th ACM Conference on Recommender Systems . New York:ACM Press , 2020 : 579 - 581 .

RIBERO M , HENDERSON J , WILLIAMSON S , et al . Federating recommendations using differentially private prototypes [J ] . arXiv preprint , 2020 ,arXiv:200300602.

HU H S , DOBBIE G , SALCIC Z , et al . A locality sensitive hashing based approach for federated recommender system [C ] // Proceedings of 2020 20th IEEE/ACM International Symposium on Cluster,Cloud and Internet Computing . Piscataway:IEEE Press , 2020 : 836 - 842 .

BLANCHARD P , MHAMDI E M E , GUERRAOUI R , et al . Machine learning with adversaries:Byzantine tolerant gradient descent [C ] // Proceedings of the 31st Annual Conference on Neural Information Processing Systems.Red Hook:Curran Associates Inc . , 2017 : 118 - 128 .

MHAMDI E M E , GUERRAOUI R , ROUAULT S E B . The hidden vulnerability of distributed learning in Byzantium [C ] // Proceedings of the 34th International Conference on Machine Learning . New York:ACM Press , 2018 .

WANG H , YUROCHKIN M , SUN Y , et al . Federated learning with matched averaging [J ] . arXiv preprint,2020,arXiv:2002.06440 .

YAN B J , LIU B Y , WANG L J , et al . FedCM:a real-time contribution measurement method for participants in federated learning [C ] // Proceedings of 2021 International Joint Conference on Neural Networks . Piscataway:IEEE Press , 2021 : 1 - 8 .

WANG G , DANG C X , ZHOU Z Y . Measure contribution of participants in federated learning [C ] // Proceedings of 2019 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2019 : 2597 - 2604 .

ZHANG J F , LI C , ROBLES-KELLY A , , et al . Hierarchically fair federated learning [J ] . arXiv preprint,2020,arXiv:2004.10386 .

Views

1271

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Big Data and Recommendation System

High resolution remote sensing image object detection algorithm based on improved YOLOv8

Research on data asset circulation and trading scheme integrating blockchain and federated learning technologies

A survey of sand-dust image visual enhancement techniques

A survey of emotional speech synthesis

Related Author

Cuiping LI

Mengwei Lan

Benyou Zou

Shaoqing Wang

Kankan Zhao

QIAO Huanyu

CAO Feng

ZHANG Xia

Related Institution

School of Computer and Information Technology, Shanxi University

School of Cyber Science and Engineering, Ningbo University of Technology

Postdoctoral Research Center, Industrial and Commercial Bank of China

Hebei University of Technology

Key Laboratory of Key Technologies for Open Data Fusion in Zhejiang Province

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273122 Visits Today：28

⁰