Otwarty dostęp

Fed-UserPro: A user profile construction method based on federated learning


Zacytuj

Kairouz P, McMahan H B, Avent B, et al. (2020), Advances and Open Problems in Federated Learning. Foundations and Trends in Machine Learning, 14(1-2):1-210 Search in Google Scholar

Li Q, Wen Z, Wu Z, et al. (2019), A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. CoRR abs/1907.09693 Search in Google Scholar

Aledhari M, Razzak R, Parizi R M, et al. (2020), Federated learning: A survey on Enabling Technologies, Protocols, and Applications. IEEE Access, 8: 140699-140725 Search in Google Scholar

McMahan B, Moore E, Ramage D, et al. (2017), Communication-efficient Learning of Deep Networks from Decentralized Data. AISTATS: 1273-1282 Search in Google Scholar

Yang Q, Liu Y, Chen T, et al. (2019), Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology, 10(2):1-19 Search in Google Scholar

Li T, Sahu A K, Talwalkar A, et al. (2020), Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3): 50-60. Search in Google Scholar

Li T, Sahu A K, Zaheer M, et al. (2020), Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2: 429-450 Search in Google Scholar

Cooper A, Reimann M R. (2005), Software concept revolution: the essence of interaction design. Beijing: Electronic Industry Press Search in Google Scholar

Lin Y, Xie X. (2018), User Portrait of Diversified Groups in Micro-blog Based on Social Identity Theory. Information Studies: Theory & Application. 041(003):142-148 Search in Google Scholar

Konečný J, McMahan H B, Yu F X, et al. (2016), Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 Search in Google Scholar

Liu Y, Kang Y, Xing C, et al. (2018), Secure Federated Transfer Learning. arXiv preprint arXiv:1812.03337 Search in Google Scholar

Zeng Z, Sun S. (2020), Research on Personalized Mobile Visual Search of Smart Library Based on User Portrait. Library & Information, (4):8 Search in Google Scholar

He Z, Zhu Q, Bai M. (2021), The Construction of Urban Elderly User Portrait from the Perspective of Pension Service. Journal of Intelligence, 40(09):154-160 Search in Google Scholar

Ren Z, Zhang P, Lan Y, et al. (2019), Emotional Tendency Prediction of Emergencies Based on the Portraits of Weibo Users Taking “8 12” Accident in Tianjin as an Example. Journal of Intelligence, 38(11):130-137 Search in Google Scholar

Blei D M, Ng A Y, Jordan M I. (2003), Latent Dirichlet allocation. Journal of Machine Learning Research, 3: 993-1022 Search in Google Scholar

Zhao Y, Li M, Lai L, et al. (2018), Federated learning with non-IID data. arXiv preprint arXiv:1806.00582 Search in Google Scholar

Bonawitz K, Eichner H, Grieskamp W, et al. (2019), Towards Federated Learning at Scale: System Design. CoRR abs/1902.01046 Search in Google Scholar

Sattler F, Müller K R, Samek W. (2021), Clustered Federated Learning: Model-Agnostic Distributed Multi-task Optimization under Privacy Constraints. IEEE Transactions on Neural Networks and Learning Systems 32(8):3710-3722 Search in Google Scholar

Liu L, Zheng F. (2021), A Bayesian Federated Learning Framework with Multivariate Gaussian Product. CoRR abs/2102.01936 Search in Google Scholar

Wang J, Kong L, Huang Z, et al. (2020), Research review of federated learning algorithms. Big Data Research, 6(6):64-82 Search in Google Scholar

Hahn S J, Lee J. (2019), Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Clinical Data. CoRR abs/1910.08489 Search in Google Scholar

Nilsson A, Smith S, Ulm G, et al. (2018), A Performance Evaluation of Federated Learning Algorithms. DIDL at Middleware: 1-8 Search in Google Scholar

Sahu A K, Li T, Sanjabi M, et al. (2018), On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR abs/1812.06127 Search in Google Scholar

eISSN:
2444-8656
Język:
Angielski
Częstotliwość wydawania:
Volume Open
Dziedziny czasopisma:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics