Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1-2):1-210, 2021.
Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. On-device federated learning via blockchain and its latency analysis. arXiv preprint arXiv:1808.03949, 2018.
Beatrice Laurent and Pascal Massart. Adaptive estimation of a quadratic functional by model selection. Annals of Statistics, pages 1302-1338, 2000.
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189, 2019.
Li Li, Yuxi Fan, Mike Tse, and Kuo-Yi Lin. A review of applications in federated learning. Computers & Industrial Engineering, 149:106854, 2020.
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3):50-60, 2020.
Zitao Li, Bolin Ding, Ce Zhang, Ninghui Li, and Jingren Zhou. Federated matrix factorization with privacy guarantee. Proceedings of the VLDB Endowment, 15(4):900-913, 2021.
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu. Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent. Advances in Neural Information Processing Systems, 30, 2017.
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273-1282. PMLR, 2017.
SA Nene, SK Nayar, and H Murase. Columbia university image library (coil-20). Technical Report CUCS-005-96, 1996.
Andrew Ng, Michael Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems, volume 14. MIT Press, 2001.
Efhymios Tzinis, Jonah Casebeer, Zhepei Wang, and Paris Smaragdis. Separate but together: Unsupervised federated learning for speech enhancement from non-iid data. In 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pages 46-50. IEEE, 2021.
Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.
Ulrike Von Luxburg. A tutorial on spectral clustering. Statistics and computing, 17(4):395-416, 2007.
Shuai Wang and Tsung-Hui Chang. Federated matrix factorization: Algorithm design and application to data clustering. IEEE Transactions on Signal Processing, 70:1625-1640, 2022.
Hongtao Wang, Ang Li, Bolin Shen, Yuyan Sun, and Hongmei Wang. Federated multi-view spectral clustering. IEEE Access, 8:202249-202259, 2020.
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays. Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903, 2018.
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1-19, 2019.
Enyue Yang, Yunfeng Huang, Feng Liang, Weike Pan, and Zhong Ming. Fcmf: Federated collective matrix factorization for heterogeneous collaborative filtering. Knowledge-Based Systems, 220:106946, 2021.