Fetching the paper…
Reading the bibliography…
Federated learning has recently been applied to recommendation systems to protect user privacy.
W. R. Thompson, “On the likelihood that one unknown probability exceeds another in view of the evidence of two samples,” Biometrika , vol. 25, no. 3-4, pp. 285–294, 1933
1933
Earlier work this paper cites.
P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in International conference on the theory and applications of cryptographic techniques . Springer, 1999, pp. 223–238
1999
Earlier work this paper cites.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Analysis of recommendation algorithms for e-commerce,” in Proceedings of the 2nd ACM Conference on Electronic Commerce , 2000, pp. 158–167
2000
Earlier work this paper cites.
J. B. Schafer, J. A. Konstan, and J. Riedl, “E-commerce recommendation applications,” Data mining and knowledge discovery , vol. 5, no. 1, pp. 115–153, 2001
2001
Earlier work this paper cites.
G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering , vol. 17, no. 6, pp. 734–749, 2005
2005
Earlier work this paper cites.
C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” in Proceedings of the 14th international conference on World Wide Web , 2005, pp. 22–32
2005
Earlier work this paper cites.
Koren, Yehuda, Bell, Robert, Volinsky, and Chris, “Matrix factorization techniques for recommender systems.” Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
Earlier work this paper cites.
J. Liu, P. Dolan, and E. R. Pedersen, “Personalized news recommendation based on click behavior,” in Proceedings of the 15th international conference on Intelligent user interfaces , 2010, pp. 31–40
2010
Earlier work this paper cites.
HetRec ’11: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems . New York, NY, USA: Association for Computing Machinery, 2011
2011
Earlier work this paper cites.
C. Dwork, “Calibrating noise to sensitivity in private data analysis,” Lecture Notes in Computer Science , vol. 3876, no. 8, pp. 265–284, 2012
2012
Earlier work this paper cites.
J. Tang, H. Gao, and H. Liu, “Mtrust: Discerning multi-faceted trust in a connected world,” in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining , ser. WSDM ’12. New York, NY, USA: Association for Computing Machinery, 2012, p. 93–102. [Online]. Available: https://doi.org/10.1145/2124295.2124309
2012
Earlier work this paper cites.
P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck, “Learning deep structured semantic models for web search using clickthrough data,” in Proceedings of the 22nd ACM international conference on Information & Knowledge Management , 2013, pp. 2333–2338
2013
Earlier work this paper cites.
G. Guo, J. Zhang, and N. Yorke-Smith, “A novel bayesian similarity measure for recommender systems.” in IJCAI , vol. 13, 2013, pp. 2619–2625
2013
Earlier work this paper cites.
R. Ormándi, I. Hegedűs, and M. Jelasity, “Gossip learning with linear models on fully distributed data,” Concurrency and Computation: Practice and Experience , vol. 25, no. 4, pp. 556–571, 2013
2013
Earlier work this paper cites.
J. Kim, D. Lee, and K.-Y. Chung, “Item recommendation based on context-aware model for personalized u-healthcare service,” Multimedia Tools and Applications , vol. 71, no. 2, pp. 855–872, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” ACM Trans. Interact. Intell. Syst. , vol. 5, no. 4, dec 2015. [Online]. Available: https://doi.org/10.1145/2827872
2015
Earlier work this paper cites.
J. P. Albrecht, “How the gdpr will change the world,” Eur. Data Prot. L. Rev. , vol. 2, p. 287, 2016
2016
Earlier work this paper cites.
C.-L. Liao and S.-J. Lee, “A clustering based approach to improving the efficiency of collaborative filtering recommendation,” Electronic Commerce Research and Applications , vol. 18, pp. 1–9, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Yang, D. Zhang, and B. Qu, “Participatory cultural mapping based on collective behavior data in location-based social networks,” ACM Trans. Intell. Syst. Technol. , vol. 7, no. 3, jan 2016. [Online]. Available: https://doi.org/10.1145/2814575
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proceedings of the 26th international conference on world wide web , 2017, pp. 173–182
2017
Earlier work this paper cites.
A. Abbas, A. Hidayet, U. A. Selcuk, and C. Mauro, “A survey on homomorphic encryption schemes: Theory and implementation,” Acm Computing Surveys , vol. 51, no. 4, pp. 1–35, 2017
2017
Earlier work this paper cites.
J. H. Cheon, A. Kim, M. Kim, and Y. Song, “Homomorphic encryption for arithmetic of approximate numbers,” in International conference on the theory and application of cryptology and information security . Springer, 2017, pp. 409–437
2017
Earlier work this paper cites.
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning . PMLR, 2017, pp. 1126–1135
2017
Earlier work this paper cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , 2017, pp. 1175–1191
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li, “Drn: A deep reinforcement learning framework for news recommendation,” in Proceedings of the 2018 world wide web conference , 2018, pp. 167–176
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Fang, G. Yang, N. Z. Gong, and J. Liu, “Poisoning attacks to graph-based recommender systems,” in Proceedings of the 34th annual computer security applications conference , 2018, pp. 381–392
2018
Earlier work this paper cites.
D. Yin, Y. Chen, R. Kannan, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in International Conference on Machine Learning . PMLR, 2018, pp. 5650–5659
2018
Cited alongside, same era.
R. Guerraoui, S. Rouault et al. , “The hidden vulnerability of distributed learning in byzantium,” in International Conference on Machine Learning . PMLR, 2018, pp. 3521–3530
2018
Cited alongside, same era.
H. Wang, S. Sievert, S. Liu, Z. Charles, D. Papailiopoulos, and S. Wright, “Atomo: Communication-efficient learning via atomic sparsification,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Cited alongside, same era.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology , vol. 10, no. 2, pp. 1–19, 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
WuChuhan, WuFangzhao, LyuLingjuan, HuangYongfeng, and XieXing, “Fedctr: Federated native ad ctr prediction with cross platform user behavior data,” ACM Transactions on Intelligent Systems and Technology (TIST) , 2021
2021
Later among the works it cites.
S. Kalloori and S. Klingler, “Horizontal cross-silo federated recommender systems,” in Fifteenth ACM Conference on Recommender Systems , 2021, pp. 680–684
2021
Later among the works it cites.
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
I. Hegedűs, G. Danner, and M. Jelasity, “Decentralized recommendation based on matrix factorization: a comparison of gossip and federated learning,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2019, pp. 317–332
2019
Cited alongside, same era.
2019
Cited alongside, same era.
K. Dolui, I. Cuba Gyllensten, D. Lowet, S. Michiels, H. Hallez, and D. Hughes, “Towards privacy-preserving mobile applications with federated learning: The case of matrix factorization (poster),” in Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services , 2019, pp. 624–625
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in ICC 2019-2019 IEEE international conference on communications (ICC) . IEEE, 2019, pp. 1–7
2019
Cited alongside, same era.
Sijing, Duan, Deyu, Zhang, Yanbo, Wang, Lingxiang, Li, Yaoxue, and Zhang, “Jointrec: A deep-learning-based joint cloud video recommendation framework for mobile iot,” IEEE Internet of Things Journal , vol. PP, no. 99, pp. 1–1, 2019
2019
Cited alongside, same era.
F. Liang, W. Pan, and Z. Ming, “Fedrec++: Lossless federated recommendation with explicit feedback,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 5, 2021, pp. 4224–4231
2021
Later among the works it cites.
J. Zhang and Y. Jiang, “A vertical federation recommendation method based on clustering and latent factor model,” in 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS) . IEEE, 2021, pp. 362–366
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Lin, W. Pan, and Z. Ming, “Fr-fmss: federated recommendation via fake marks and secret sharing,” in Fifteenth ACM Conference on Recommender Systems , 2021, pp. 668–673
2021
Later among the works it cites.
2021
Later among the works it cites.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al. , “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning , vol. 14, no. 1–2, pp. 1–210, 2021
2021
Later among the works it cites.
J. Wu, Q. Liu, Z. Huang, Y. Ning, H. Wang, E. Chen, J. Yi, and B. Zhou, “Hierarchical personalized federated learning for user modeling,” in Proceedings of the Web Conference 2021 , 2021, pp. 957–968
2021
Later among the works it cites.
Q. Ma, Y. Xu, H. Xu, Z. Jiang, L. Huang, and H. Huang, “Fedsa: A semi-asynchronous federated learning mechanism in heterogeneous edge computing,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 12, pp. 3654–3672, 2021
2021
Later among the works it cites.
V. W. Anelli, Y. Deldjoo, T. D. Noia, A. Ferrara, and F. Narducci, “Federank: User controlled feedback with federated recommender systems,” in European Conference on Information Retrieval . Springer, 2021, pp. 32–47
2021
Later among the works it cites.
B. Acun, M. Murphy, X. Wang, J. Nie, C.-J. Wu, and K. Hazelwood, “Understanding training efficiency of deep learning recommendation models at scale,” in 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA) . IEEE, 2021, pp. 802–814
2021
Later among the works it cites.
J. Qin, B. Liu, and J. Qian, “A novel privacy-preserved recommender system framework based on federated learning,” in 2021 The 4th International Conference on Software Engineering and Information Management , 2021, pp. 82–88
2021
Later among the works it cites.
F. K. Khan, A. Flanagan, K. E. Tan, Z. Alamgir, and M. Ammad-Ud-Din, “A payload optimization method for federated recommender systems,” in Fifteenth ACM Conference on Recommender Systems , 2021, pp. 432–442
2021
Later among the works it cites.
Y. Song, Y. Xie, H. Zhang, Y. Liang, X. Ye, A. Yang, and Y. Ouyang, “Federated learning application on telecommunication-joint healthcare recommendation,” in 2021 IEEE 21st International Conference on Communication Technology (ICCT) , 2021, pp. 1443–1448
2021
Later among the works it cites.
L.-e. Wang, Y. Wang, Y. Bai, P. Liu, and X. Li, “Poi recommendation with federated learning and privacy preserving in cross domain recommendation,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) , 2021, pp. 1–6
2021
Later among the works it cites.
S. Warnat-Herresthal, H. Schultze, K. L. Shastry, S. Manamohan, S. Mukherjee, V. Garg, R. Sarveswara, K. Händler, P. Pickkers, N. A. Aziz et al. , “Swarm learning for decentralized and confidential clinical machine learning,” Nature , vol. 594, no. 7862, pp. 265–270, 2021
2021
Later among the works it cites.
T. Qi, F. Wu, C. Wu, Y. Huang, and X. Xie, “Uni-FedRec: A unified privacy-preserving news recommendation framework for model training and online serving,” in Findings of the Association for Computational Linguistics: EMNLP 2021 . Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 1438–1448. [Online]. Available: https://aclanthology.org/2021.findings-emnlp.124
2021
Later among the works it cites.
S. Zhang, H. Yin, T. Chen, Z. Huang, Q. V. H. Nguyen, and L. Cui, “Pipattack: Poisoning federated recommender systems for manipulating item promotion,” in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining , 2022, pp. 1415–1423
2022
Closest in time.
C. Wu, F. Wu, T. Qi, Y. Huang, and X. Xie, “Fedattack: Effective and covert poisoning attack on federated recommendation via hard sampling,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 4164–4172. [Online]. Available: https://doi.org/10.1145/3534678.3539119
2022
Closest in time.
L. Lyu, H. Yu, X. Ma, C. Chen, L. Sun, J. Zhao, Q. Yang, and S. Y. Philip, “Privacy and robustness in federated learning: Attacks and defenses,” IEEE Transactions on Neural Networks and Learning Systems , 2022
2022
Closest in time.
L. Yang, Y. Yu, and Y. Wei, “Data-driven artificial intelligence recommendation mechanism in online learning resources,” International Journal of Crowd Science , vol. 6, no. 3, pp. 150–157, 2022
2022
Closest in time.
V. Perifanis and P. S. Efraimidis, “Federated neural collaborative filtering,” Knowledge-Based Systems , vol. 242, p. 108441, 2022
2022
Closest in time.
M. Imran, H. Yin, T. Chen, N. Q. V. Hung, A. Zhou, and K. Zheng, “Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences,” ACM Trans. Inf. Syst. , aug 2022, just Accepted. [Online]. Available: https://doi.org/10.1145/3560486
2022
Closest in time.
Z. Liu, L. Yang, Z. Fan, H. Peng, and P. S. Yu, “Federated social recommendation with graph neural network,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 13, no. 4, pp. 1–24, 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Q. Wang, H. Yin, T. Chen, J. Yu, A. Zhou, and X. Zhang, “Fast-adapting and privacy-preserving federated recommender system,” The VLDB Journal , vol. 31, no. 5, pp. 877–896, 2022
2022
Closest in time.
Z. Jie, S. Chen, J. Lai, M. Arif, and Z. He, “Personalized federated recommendation system with historical parameter clustering,” Journal of Ambient Intelligence and Humanized Computing , pp. 1–11, 2022
2022
Closest in time.
S. Luo, Y. Xiao, and L. Song, “Personalized federated recommendation via joint representation learning, user clustering, and model adaptation,” in Proceedings of the 31st ACM International Conference on Information and Knowledge Management . New York, NY, USA: Association for Computing Machinery, 2022, p. 4289–4293. [Online]. Available: https://doi.org/10.1145/3511808.3557668
2022
Closest in time.
Z. Ai, G. Wu, B. Li, Y. Wang, and C. Chen, “Fourier enhanced mlp with adaptive model pruning for efficient federated recommendation,” in International Conference on Knowledge Science, Engineering and Management . Springer, 2022, pp. 356–368
2022
Closest in time.