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To support large-scale model training, split learning (SL) enables multiple edge devices/servers to share the intensive training workload.
A. Frieze and J. Yadegar, “On the quadratic assignment problem,” Discrete Applied Mathematics , vol. 5, no. 1, pp. 89–98, Sep., 1983
1983
Earlier work this paper cites.
M. Minoux, “Solving combinatorial problems with combined min-max-min-sum objective and applications,” Mathematical Programming , vol. 45, pp. 361–372, Aug. 1989
1989
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE , vol. 86, no. 11, pp. 2278–2324, Nov. 1998
1998
Earlier work this paper cites.
L. Grippo and M. Sciandrone, “On the convergence of the block nonlinear gauss–seidel method under convex constraints,” Oper. Res. Lett. , vol. 26, no. 3, pp. 127–136, Apr., 2000
2000
Earlier work this paper cites.
P. Tseng, “Convergence of a block coordinate descent method for nondifferentiable minimization,” J Optim Theory Appl , vol. 109, pp. 475–494, Jun. 2001
2001
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , vol. 25. Curran Associates, Inc., 2012, pp. 1097–1105
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
H. He, H. Daumé, and J. Eisner, “Learning to search in branch-and-bound algorithms,” in Proceedings of the International Conference on Neural Information Processing Systems . Montreal, Canada: MIT Press, Dec., 2014, p. 3293–3301
2014
Earlier work this paper cites.
M. K. Samimi, T. S. Rappaport, and G. R. MacCartney, “Probabilistic omnidirectional path loss models for millimeter-wave outdoor communications,” IEEE Wireless Commun. Lett. , vol. 4, no. 4, pp. 357–360, Aug., 2015
2015
Earlier work this paper cites.
Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Communications Surveys & Tutorials , vol. 19, no. 4, pp. 2322–2358, Aug., 2017
2017
Earlier work this paper cites.
T. Taleb, S. Dutta, A. Ksentini, M. Iqbal, and H. Flinck, “Mobile edge computing potential in making cities smarter,” IEEE Communications Magazine , vol. 55, no. 3, pp. 38–43, 2017
2017
Earlier work this paper cites.
P. Voigt and A. Von dem Bussche, “The EU general data protection regulation (GDPR),” A Practical Guide, 1st Ed., Cham: Springer International Publishing , vol. 10, no. 3152676, pp. 10–5555, 2017
2017
Earlier work this paper cites.
NVIDIA Corporation, “NVIDIA Tesla V100 GPU Architecture,” 2017. [Online]. Available: https://images.nvidia.com/content/volta-architecture/pdf/volta-architecture-whitepaper.pdf
2017
Earlier work this paper cites.
Y. Kang, J. Hauswald, C. Gao, A. Rovinski, T. Mudge, J. Mars, and L. Tang, “Neurosurgeon: Collaborative intelligence between the cloud and mobile edge,” in Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems , New York, NY, USA, Apr., 2017, p. 615–629
2017
Earlier work this paper cites.
O. Gupta and R. Raskar, “Distributed learning of deep neural network over multiple agents,” Journal of Network and Computer Applications , vol. 116, pp. 1–8, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Y. Huang, Y. Cheng, A. Bapna, O. Firat, D. Chen, M. Chen, H. Lee, J. Ngiam, Q. V. Le, Y. Wu et al. , “GPipe: Efficient training of giant neural networks using pipeline parallelism,” in Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , Vancouver, BC, Canada, Dec., 2019, pp. 103–112
2019
Earlier work this paper cites.
D. Narayanan, A. Harlap, A. Phanishayee, V. Seshadri, N. R. Devanur, G. R. Ganger, P. B. Gibbons, and M. Zaharia, “PipeDream: Generalized pipeline parallelism for DNN training,” in Proceedings of the ACM Symposium on Operating Systems Principles , New York, NY, USA, Oct., 2019, p. 1–15
2019
Earlier work this paper cites.
C. Hu, W. Bao, D. Wang, and F. Liu, “Dynamic adaptive DNN surgery for inference acceleration on the edge,” in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications , Paris, France, Jun., 2019, pp. 1423–1431
2019
Earlier work this paper cites.
X. Hu, L. Wang, K.-K. Wong, M. Tao, Y. Zhang, and Z. Zheng, “Edge and central cloud computing: A perfect pairing for high energy efficiency and low-latency,” IEEE Trans. Wireless Commun. , vol. 19, no. 2, pp. 1070–1083, Feb., 2019
2019
Earlier work this paper cites.
T. Qiu, J. Chi, X. Zhou, Z. Ning, M. Atiquzzaman, and D. O. Wu, “Edge computing in industrial internet of things: Architecture, advances and challenges,” IEEE Communications Surveys & Tutorials , vol. 22, no. 4, pp. 2462–2488, 2020
2020
Earlier work this paper cites.
Z. Ning, P. Dong, X. Wang, X. Hu, L. Guo, B. Hu, Y. Guo, T. Qiu, and R. Y. K. Kwok, “Mobile edge computing enabled 5G health monitoring for internet of medical things: A decentralized game theoretic approach,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 2, pp. 463–478, 2021
2021
Cited alongside, same era.
Y. J. Ha, M. Yoo, S. Park, S. Jung, and J. Kim, “Secure aerial surveillance using split learning,” in 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) . IEEE, 2021, pp. 434–437
2021
Cited alongside, same era.
Raspberry Pi Foundation, “Raspberry Pi 4 model B specifications,” 2021. [Online]. Available: https://www.raspberrypi.com/products/raspberry-pi-4-model-b/specifications/
2021
Cited alongside, same era.
S. Li and T. Hoefler, “Chimera: Efficiently training large-scale neural networks with bidirectional pipelines,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis . New York, NY, USA: Association for Computing Machinery, Nov., 2021, pp. 1–14
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Lin, G. Qu, X. Chen, and K. Huang, “Split learning in 6g edge networks,” IEEE Wireless Communications , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Lin, G. Qu, X. Chen, and K. Huang, “Split learning in 6G edge networks,” IEEE Wireless Communications , vol. 31, no. 4, pp. 170–176, Aug., 2024
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2021
Cited alongside, same era.
2021
Cited alongside, same era.
S. Fan, Y. Rong, C. Meng, Z. Cao, S. Wang, Z. Zheng, C. Wu, G. Long, J. Yang, L. Xia, L. Diao, X. Liu, and W. Lin, “DAPPLE: A pipelined data parallel approach for training large models,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming . New York, NY, USA: Association for Computing Machinery, Feb. 2021, p. 431–445
2021
Cited alongside, same era.
2021
Cited alongside, same era.
J. Ren, G. Yu, and G. Ding, “Accelerating DNN training in wireless federated edge learning systems,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 1, pp. 219–232, Nov. 2021
2021
Cited alongside, same era.
S. Wang, X. Zhang, H. Uchiyama, and H. Matsuda, “Hivemind: Towards cellular native machine learning model splitting,” IEEE Journal on Selected Areas in Communications , vol. 40, no. 2, pp. 626–640, Feb. 2022
2022
Cited alongside, same era.
W. Wei, J. Wang, J. Du, Z. Fang, C. Jiang, and Y. Ren, “Underwater differential game: Finite-time target hunting task with communication delay,” in IEEE International Conference on Communications , Seoul, Korea, May, 2022, pp. 3989–3994
2022
Cited alongside, same era.
W. Zhou, Z. Qu, Y. Zhao, B. Tang, and B. Ye, “An efficient split learning framework for recurrent neural network in mobile edge environment,” in Proceedings of the Conference on Research in Adaptive and Convergent Systems , New York, NY, USA, Oct., 2022, p. 131–138
2022
Cited alongside, same era.
G. Yeung, D. Borowiec, R. Yang, A. Friday, R. Harper, and P. Garraghan, “Horus: Interference-aware and prediction-based scheduling in deep learning systems,” IEEE Transactions on Parallel and Distributed Systems , vol. 33, no. 1, pp. 88–100, Jan. 2022
2022
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Wang, Z. Zhang, J. Wang, C. Jiang, W. Wei, and Y. Ren, “AUV-assisted node repair for iout relying on multiagent reinforcement learning,” IEEE Internet of Things Journal , vol. 11, no. 3, pp. 4139–4151, Feb., 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Lin, G. Zhu, Y. Deng, X. Chen, Y. Gao, K. Huang, and Y. Fang, “Efficient parallel split learning over resource-constrained wireless edge networks,” IEEE Trans. Mobile Comput. , pp. 1–16, early access, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Gurobi Optimization, LLC, “Gurobi Optimizer Reference Manual,” 2024. [Online]. Available: https://www.gurobi.com
2024
Later among the works it cites.
Z. Lin, G. Zhu, Y. Deng, X. Chen, Y. Gao, K. Huang, and Y. Fang, “Efficient parallel split learning over resource-constrained wireless edge networks,” IEEE Transactions on Mobile Computing , vol. 23, no. 10, pp. 9224–9239, Jan.,2024
2024
Later among the works it cites.
Y. Yoo and S. Jung, “Modeling forecast errors for microgrid operation using Gaussian process regression,” Scientific Reports , vol. 14, no. 1, p. 2166, Jan., 2024
2024
Later among the works it cites.
Z. Lin, W. Wei, Z. Chen, C.-T. Lam, X. Chen, Y. Gao, and J. Luo, “Hierarchical split federated learning: Convergence analysis and system optimization,” IEEE Transactions on Mobile Computing , 2025
2025
Closest in time.
2025
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G. Qu, Q. Chen, W. Wei, Z. Lin, X. Chen, and K. Huang, “Mobile edge intelligence for large language models: A contemporary survey,” IEEE Communications Surveys & Tutorials , pp. 1–1, 2025
2025
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2025
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2025
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W. Wei, J. Wang, J. Du, Z. Fang, Y. Ren, and C. L. P. Chen, “Differential game-based deep reinforcement learning in underwater target hunting task,” IEEE Transactions on Neural Networks and Learning Systems , vol. 36, no. 1, pp. 462–474, Jan., 2025
2025
Closest in time.
HuaweiTech, “ITU-R WP5D completed the recommendation framework for IMT-2030 (global 6G vision),” 2023. [Online]. Available: https://www.huawei.com/en/huaweitech/future-technologies/itu-r-wp5d-completed-recommendation-framework-imt-2030
2030
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