Centralized Machine Learning Versus Federated Averaging: A Comparison using MNIST Dataset |
Peng, Sony
(Department of Software Convergence, Soonchunhyang University)
Yang, Yixuan (Department of Software Convergence, Soonchunhyang University) Mao, Makara (Department of Software Convergence, Soonchunhyang University) Park, Doo-Soon (Department of Computer Software Engineering, Soonchunhyang University) |
1 | Liu, Y., Kang, Y., Li, L., Zhang, X., Cheng, Y., Chen, T., ... & Yang, Q, "A Communication-Efficient Collaborative Learning Framework for Distributed Features," ArXiv abs/1912.11187, 2019. |
2 | S.K. Maity, A. Panigrahi, and A. Mukherjee, "Book reading behavior on good reads can predict the amazon best sellers," in Proc. of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 451-454, 2017. |
3 | N. Onoszko, G. Karlsson, O. Mogren, and E.L. Zec, "Decentralized federated learning of deep neural networks on non-iid data," arXiv preprint arXiv:2107.08517, 2021. |
4 | C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, "A survey on federated learning," Knowledge-Based Systems, vol. 216, pp. 106775, 2021. DOI |
5 | Y. Liu, Y. Kang, C. Xing, T. Chen, and Q. Yang, "A secure federated transfer learning framework," IEEE Intelligent Systems, vol. 35, no. 4, pp. 70-82, 2020. DOI |
6 | S. Ji, W. Jiang, A. Walid, and X. Li, "Dynamic sampling and selective masking for communication-efficient federated learning," arXiv preprint arXiv:2003.09603, 2020. |
7 | H. Zhu, H. Zhang, and Y. Jin, "From federated learning to federated neural architecture search: a survey," Complex & Intelligent Systems, vol. 7, no. 2, pp. 639-657, 2020. |
8 | T. Sun, D. Li, and B. Wang, "Decentralized Federated Averaging," arXiv preprint arXiv:2104.11375, 2021. |
9 | B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-efficient learning of deep networks from decentralized data," in Proc. of the 20 th International Conference on Artificial intelligence and statistics, pp. 1273-1282, 2017. |
10 | J.C. Jiang, B. Kantarci, S. Oktug, and T. Soyata, "Federated learning in smart city sensing: Challenges and opportunities," Sensors, vol. 20, no. 21, pp. 6230, 2020. DOI |
11 | N. Rieke, J. Hancox, W. Li, et al., "The future of digital health with federated learning," npj digital medicine, vol. 3, no. 119, 2020. |
12 | MNIST database, "THE MNIST DATABASE of handwritten digits," 2021. |
13 | D. Guliani, F. Beaufays, and G. Mott, "Training speech recognition models with federated learning: A quality/cost framework," in Proc. of 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3080-3084, 2021. |
14 | F. Yu, A.S. Rawat, A. Menon, and S. Kumar, "Federated learning with only positive labels," in Proc. of International Conference on Machine Learning, pp. 10946-10956, 2020. |
15 | S. AbdulRahman, H. Tout, H. Ould-Slimane, A. Mourad, C. Talhi, and M.A. Guizani, "A survey on federated learning: The journey from centralized to distributed on-site learning and beyond," IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5476-5497, 2021. DOI |
16 | O. Shahid, S. Pouriyeh, R.M. Parizi, Q.Z. Sheng, G. Srivastava, L. Zhao, "Communication Efficiency in Federated Learning: Achievements and Challenges," arXiv preprint arXiv:2107.10996, 2021. |
17 | A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, and D. Ramage, "Federated learning for mobile keyboard prediction," arXiv preprint arXiv:1811.03604, 2018. |
18 | Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, "Blockchain and federated learning for privacy-preserved data sharing in industrial IoT," IEEE Transactions on Industrial Informatics, vol. 16, no. 6, pp. 4177-4186, 2020. DOI |
19 | H. Yoo, R.C. Park, and K. Chung, "IoT-Based Health Big-Data Process Technologies: A Survey," KSII Transactions on Internet and Information Systems, vol. 15, no. 3, pp. 974-992, 2021. |
20 | Z. Du, C. W, T. Yoshinaga, K.A. Yau, Y. Ji, and J. Li, "Federated learning for vehicular internet of things: Recent advances and open issues," IEEE Open J. Comput. Soc., vol. 1, pp. 45-61, 2020. DOI |
21 | L. Wang, and D. Xu, "Resource allocation in downlink SWIPT-based cooperative NOMA systems," KSII Transactions on Internet and Information Systems, vol. 14, no. 1, pp. 20-39, 2020. DOI |
22 | A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, and D. Ramage, "Federated learning for mobile keyboard prediction," arXiv preprint arXiv:1811.03604, 2018. |
23 | C.C. Ma, K.M. Kuo, and J.W. Alexander, "A survey-based study of factors that motivate nurses to protect the privacy of electronic medical records," BMC medical informatics and decision making, vol. 16, no. 1, pp. 1-11, 2015. DOI |
24 | D. Lia, and M. Togan, "Privacy-Preserving Machine Learning Using Federated Learning and Secure Aggregation," in Proc. of 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1-6, 2020. |
25 | X. Li, L. Zhang, A. You, M. Yang, K. Yang, and Y. Tong, "Global aggregation then local distribution in fully convolutional networks," arXiv preprint arXiv:1909.07229, 2019. |