Built on

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient Learning of Deep Networks From Decentralized Data,” in Proc. AISTATS , Apr. 2017
2017
Earlier work this paper cites.
P. Vepakomma, O. Gupta, T. Swedish, and R. Raskar, “Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data,” arXiv preprint arXiv:1812.00564 , Dec. 2018
Original
2018
Earlier work this paper cites.
J. Shao and J. Zhang, “Communication-computation Trade-off in Resource-constrained Edge Inference,” IEEE Commun. Mag. , vol. 58, no. 12, pp. 20–26, Dec. 2020
2020
Earlier work this paper cites.
L. Deng, G. Li, S. Han, L. Shi, and Y. Xie, “Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey,” Proc IEEE Inst Electr Electron Eng , vol. 108, no. 4, pp. 485–532, Apr. 2020
2020
Earlier work this paper cites.
3GPP. “Study on Traffic Characteristics and Performance Requirements for AI/ML Model Transfer in 5GS”. 3rd Generation Partnership Project (3GPP), Technical Specification (TS) 22.874, 2021, version 18.2.0., Dec. 2021
2021
Earlier work this paper cites.

Similar

Huawei, 6G: The Next Horizon: From Connected People and Things to Connected Intelligence . Cambridge, U.K.: Cambridge Univ. Press, 2021
2021
Cited alongside, same era.
P. Joshi, C. Thapa, S. Camtepe, M. Hasanuzzamana, T. Scully, and H. Afli, “Splitfed Learning Without Client-side Synchronization: Analyzing Client-side Split Network Portion Size to Overall Performance,” arXiv preprint arXiv:2109.09246 , Sep. 2021
Original
2021
Cited alongside, same era.
C. Thapa, P. C. M. Arachchige, S. Camtepe, and L. Sun, “Splitfed: When Federated Learning Meets Split Learning,” in Proc. AAAI , Feb. 2022
2022
Cited alongside, same era.
C.-Y. Hsieh, Y.-C. Chuang, and A.-Y. Wu, “C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning,” in Proc. MLSP , Aug. 2022
2022
Cited alongside, same era.

Then

S. Wang, X. Zhang, H. Uchiyama, and H. Matsuda, “HiveMind: Towards Cellular Native Machine Learning Model Splitting,” IEEE J. Sel. Areas Commun. , vol. 40, no. 2, pp. 626–640, Feb. 2022
2022
Later among the works it cites.
M. Kim, A. DeRieux, and W. Saad, “A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networks,” in Proc. WCNC , Mar. 2023
2023
Closest in time.
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,” arXiv preprint arXiv:2303.15991 , Mar. 2023
Original
2023
Closest in time.
Z. Lin, G. Qu, Q. Chen, X. Chen, Z. Chen, and K. Huang, “Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities,” arXiv preprint arXiv:2309.16739 , Oct. 2023
Original
2023
Closest in time.

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2023

Split Learning in 6G Edge Networks

Lin, Zheng, Qu, Guanqiao, Chen, Xianhao et al.

Understand

With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence.

Along this line, the proposal to incorporate federated learning into the mobile edge has gained considerable interest in recent years.
However, the deployment of federated learning faces substantial challenges as massive resource-limited IoT devices can hardly support on-device model training.
This leads to the emergence of split learning (SL) which enables servers to handle the major training workload while still enhancing data privacy.

Built on

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient Learning of Deep Networks From Decentralized Data,” in Proc. AISTATS , Apr. 2017
2017
Earlier work this paper cites.
P. Vepakomma, O. Gupta, T. Swedish, and R. Raskar, “Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data,” arXiv preprint arXiv:1812.00564 , Dec. 2018
Original
2018
Earlier work this paper cites.
J. Shao and J. Zhang, “Communication-computation Trade-off in Resource-constrained Edge Inference,” IEEE Commun. Mag. , vol. 58, no. 12, pp. 20–26, Dec. 2020
2020
Earlier work this paper cites.
L. Deng, G. Li, S. Han, L. Shi, and Y. Xie, “Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey,” Proc IEEE Inst Electr Electron Eng , vol. 108, no. 4, pp. 485–532, Apr. 2020
2020
Earlier work this paper cites.
3GPP. “Study on Traffic Characteristics and Performance Requirements for AI/ML Model Transfer in 5GS”. 3rd Generation Partnership Project (3GPP), Technical Specification (TS) 22.874, 2021, version 18.2.0., Dec. 2021
2021
Earlier work this paper cites.

Similar

Huawei, 6G: The Next Horizon: From Connected People and Things to Connected Intelligence . Cambridge, U.K.: Cambridge Univ. Press, 2021
2021
Cited alongside, same era.
P. Joshi, C. Thapa, S. Camtepe, M. Hasanuzzamana, T. Scully, and H. Afli, “Splitfed Learning Without Client-side Synchronization: Analyzing Client-side Split Network Portion Size to Overall Performance,” arXiv preprint arXiv:2109.09246 , Sep. 2021
Original
2021
Cited alongside, same era.
C. Thapa, P. C. M. Arachchige, S. Camtepe, and L. Sun, “Splitfed: When Federated Learning Meets Split Learning,” in Proc. AAAI , Feb. 2022
2022
Cited alongside, same era.
C.-Y. Hsieh, Y.-C. Chuang, and A.-Y. Wu, “C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning,” in Proc. MLSP , Aug. 2022
2022
Cited alongside, same era.

Then

S. Wang, X. Zhang, H. Uchiyama, and H. Matsuda, “HiveMind: Towards Cellular Native Machine Learning Model Splitting,” IEEE J. Sel. Areas Commun. , vol. 40, no. 2, pp. 626–640, Feb. 2022
2022
Later among the works it cites.
M. Kim, A. DeRieux, and W. Saad, “A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networks,” in Proc. WCNC , Mar. 2023
2023
Closest in time.
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,” arXiv preprint arXiv:2303.15991 , Mar. 2023
Original
2023
Closest in time.
Z. Lin, G. Qu, Q. Chen, X. Chen, Z. Chen, and K. Huang, “Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities,” arXiv preprint arXiv:2309.16739 , Oct. 2023
Original
2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

X. Chen, G. Zhu, Y. Deng, and Y. Fang, “Federated Learning over Multihop Wireless Networks With In-Network Aggregation,” IEEE Trans. Wirel. Commun. , vol. 21, no. 6, pp. 4622–4634, Apr. 2022

2022

Cited alongside, same era.

W. Wu, M. Li, K. Qu, C. Zhou, X. Shen, W. Zhuang, X. Li, and W. Shi, “Split Learning over Wireless Networks: Parallel Design and Resource Management,” IEEE J. Sel. Areas Commun. , vol. 41, no. 4, pp. 1051–1066, Apr. 2023

2023

Closest in time.