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Machine Learning at the Edge
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=='''References'''== [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818760&tag=1 1. M. Zhang, X. Shen, J. Cao, Z. Cui and S. Jiang, "EdgeShard: Efficient LLM Inference via Collaborative Edge Computing," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2024.3524255] [https://ieeexplore.ieee.org/abstract/document/8690980 2. X. Chen, H. Zhang, C. Wu, S. Mao, Y. Ji and M. Bennis, "Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning," 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA, 2018, pp. 1-6, doi: 10.1109/VTCFall.2018.8690980.] [https://ieeexplore.ieee.org/abstract/document/8976180 3. X. Wang, Y. Han, V. C. M. Leung, D. Niyato, X. Yan and X. Chen, "Convergence of Edge Computing and Deep Learning: A Comprehensive Survey," in IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 869-904, Secondquarter 2020, doi: 10.1109/COMST.2020.2970550.] [https://dl.acm.org/doi/abs/10.1145/3093337.3037698 4. Kang, Yiping and Hauswald, Johann and Gao, Cao and Rovinski, Austin and Mudge, Trevor and Mars, Jason and Tang, Lingjia, "Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge" 2017 Association for Computing Machinery, New York, NY, USA, 2017, doi: 10.1145/3093337.3037698.] [https://dl.acm.org/doi/pdf/10.1145/3555802 5. H. Hua, Y. Li, T. Wang, N. Dong, W. Li, and J. Cao, "Edge computing with artificial intelligence: A machine learning perspective," ACM Computing Surveys, vol. 55, no. 9, Art. no. 184, pp. 1β35, Jan. 2023, doi: 10.1145/3555802..] [https://www.mdpi.com/2079-9292/13/3/640 6. Grzesik, Piotr, and Dariusz Mrozek. "Combining machine learning and edge computing: Opportunities, challenges, platforms, frameworks, and use cases." Electronics 13.3 (2024): 640.] [https://link.springer.com/chapter/10.1007/978-3-030-96756-7_1#citeas 7. S. Rafatirad, H. Homayoun, Z. Chen, and S. M. Pudukotai Dinakarrao, "What Is Applied Machine Learning?," in Machine Learning for Computer Scientists and Data Analysts, Cham, Switzerland: Springer, 2022.] [https://link.springer.com/article/10.1007/s13748-012-0035-5 8. Peteiro-Barral, Diego, and Bertha Guijarro-BerdiΓ±as. "A survey of methods for distributed machine learning." Progress in Artificial Intelligence 2 (2013): 1-11.] [https://proceedings.neurips.cc/paper/2010/hash/abea47ba24142ed16b7d8fbf2c740e0d-Abstract.html 9. Zinkevich, Martin, et al. "Parallelized stochastic gradient descent." Advances in neural information processing systems 23 (2010).] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9134370 10. F. Zhuang et al., "A Comprehensive Survey on Transfer Learning," in Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, Jan. 2021, doi: 10.1109/JPROC.2020.3004555] [https://proceedings.mlr.press/v97/phuong19a/phuong19a.pdf 11. M. Phuong and C. Lampert, "Towards understanding knowledge distillation," in Proc. Int. Conf. Mach. Learn., May 2019, pp. 5142β5151.] [https://www.jmlr.org/papers/volume18/16-456/16-456.pdf 12. I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, "Quantized neural networks: Training neural networks with low precision weights and activations," Journal of Machine Learning Research, vol. 18, no. 187, pp. 1β30, 2018.]] [https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8588318 13. D. Banik, A. Ekbal, and P. Bhattacharyya, "Machine learning based optimized pruning approach for decoding in statistical machine translation," IEEE Access, vol. 7, pp. 1736β1751, Dec. 2018, doi: 10.1109/ACCESS.2018.2883738.]
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