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Large language models (LLMs) have demonstrated remarkable success across various application domains, but their enormous sizes and computational demands pose significant challenges for deployment on resource-constrained edge devices.
A. Ruszczyński, “Feasible direction methods for stochastic programming problems,” Math. Program
1980
Earlier work this paper cites.
M. L. Overton and R. S. Womersley, “On the sum of the largest eigenvalues of a symmetric matrix,” SIAM J. Matrix Anal. Appl
1992
Earlier work this paper cites.
S. Cui, J.-J. Xiao, A. J. Goldsmith, Z.-Q. Luo, and H. V. Poor, “Energy-efficient joint estimation in sensor networks: Analog vs. digital,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
2005
Earlier work this paper cites.
Cambridge university press, 2005
D. Tse and P. Viswanath, Fundamentals of wireless communication · 2005
Earlier work this paper cites.
Cambridge university press, 2005
A. Goldsmith, Wireless communications · 2005
Earlier work this paper cites.
B. Nazer and M. Gastpar, “Computation over multiple-access channels,” IEEE Trans. Inf. Theory
2007
Earlier work this paper cites.
Z.-Q. Luo, W.-K. Ma, A. M.-C. So, Y. Ye, and S. Zhang, “Semidefinite relaxation of quadratic optimization problems,” IEEE Signal Process. Mag
2010
Earlier work this paper cites.
I. M. Bomze, V. F. Demyanov, R. Fletcher, T. Terlaky, I. Pólik, and T. Terlaky, “Interior point methods for nonlinear optimization,” Nonlinear Optimization
2010
Earlier work this paper cites.
M. Goldenbaum, H. Boche, and S. Stańczak, “Harnessing interference for analog function computation in wireless sensor networks,” IEEE Trans. Signal Process
2013
Earlier work this paper cites.
M. Grant and S. Boyd, “Cvx: Matlab software for disciplined convex programming, version 2.1,” 2014
2014
Earlier work this paper cites.
PhD thesis, University of Minnesota, 2014
M. Razaviyayn, Successive convex approximation: Analysis and applications · 2014
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Adv. Neural Inf. Process. Syst
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang, “The roadmap to 6G: AI empowered wireless networks,” IEEE Commun. Mag
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
X. Li, G. Zhu, Y. Gong, and K. Huang, “Wirelessly powered data aggregation for iot via over-the-air function computation: Beamforming and power control,” IEEE Trans. Wireless Commun
2019
Earlier work this paper cites.
A. Liu, V. K. Lau, and B. Kananian, “Stochastic successive convex approximation for non-convex constrained stochastic optimization,” IEEE Trans. Signal Process
2019
Earlier work this paper cites.
X. Cao, G. Zhu, J. Xu, and K. Huang, “Optimized power control for over-the-air computation in fading channels,” IEEE Trans. Wireless Commun
2020
Earlier work this paper cites.
K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation,” IEEE Trans. Wireless Commun
2020
Earlier work this paper cites.
Y. Gu, R. Tinn, H. Cheng, M. Lucas, N. Usuyama, X. Liu, T. Naumann, J. Gao, and H. Poon, “Domain-specific language model pretraining for biomedical natural language processing,” ACM Trans. Comput. Healthc
2021
Earlier work this paper cites.
K. B. Letaief, Y. Shi, J. Lu, and J. Lu, “Edge artificial intelligence for 6G: Vision, enabling technologies, and applications,” IEEE J. Sel. Areas Commun
2021
Earlier work this paper cites.
J. Shao, Y. Mao, and J. Zhang, “Learning task-oriented communication for edge inference: An information bottleneck approach,” IEEE J. Sel. Areas Commun
2021
Cited alongside, same era.
M. Frey, I. Bjelaković, and S. Stańczak, “Over-the-air computation in correlated channels,” IEEE Trans. Signal Process
2021
Cited alongside, same era.
T. Sery, N. Shlezinger, K. Cohen, and Y. C. Eldar, “Over-the-air federated learning from heterogeneous data,” IEEE Trans. Signal Process
2021
Cited alongside, same era.
X. Fan, Y. Wang, Y. Huo, and Z. Tian, “Joint optimization of communications and federated learning over the air,” IEEE Trans. Wireless Commun
2021
Cited alongside, same era.
F. Wang and V. K. Lau, “Multi-level over-the-air aggregation of mobile edge computing over d2d wireless networks,” IEEE Trans. Wireless Commun
2022
Cited alongside, same era.
J. Shao, J. Tong, Q. Wu, W. Guo, Z. Li, Z. Lin, and J. Zhang, “WirelessLLM: Empowering large language models towards wireless intelligence,” J. Commun. Inf. Netw
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
A. Borzunov, M. Ryabinin, A. Chumachenko, D. Baranchuk, T. Dettmers, Y. Belkada, P. Samygin, and C. A. Raffel, “Distributed inference and fine-tuning of large language models over the internet,” Adv. Neural Inf. Process. Syst
2024
Later among the works it cites.
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B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” ACM Comput. Surv
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H. Li, W. Yu, H. He, J. Shao, S. Song, J. Zhang, and K. B. Letaief, “Task-oriented communication with out-of-distribution detection: An information bottleneck framework,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Kuala Lumpur, Malaysia
2023
Cited alongside, same era.
D. Wen, X. Jiao, P. Liu, G. Zhu, Y. Shi, and K. Huang, “Task-oriented over-the-air computation for multi-device edge AI,” IEEE Trans. Wireless Commun
2023
Cited alongside, same era.
Z. Liu, Q. Lan, A. E. Kalor, P. Popovski, and K. Huang, “Over-the-air multi-view pooling for distributed sensing,” IEEE Trans. Wireless Commun
2023
Cited alongside, same era.
C. Feres, B. C. Levy, and Z. Ding, “Over-the-air multi-sensor collaboration for resource efficient joint detection,” IEEE Trans. Signal Process
2023
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
X. Yuan, N. Li, T. Zhang, M. Li, Y. Chen, J. F. M. Ortega, and S. Guo, “High efficiency inference accelerating algorithm for noma-based edge intelligence,” IEEE Trans. Wireless Commun
2024
Later among the works it cites.
Y. He, J. Fang, F. R. Yu, and V. C. Leung, “Large language models inference offloading and resource allocation in cloud-edge computing: An active inference approach,” IEEE Trans. Mobile Comput
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
J. Zhu, Y. Shi, Y. Zhou, C. Jiang, W. Chen, and K. B. Letaief, “Over-the-air federated learning and optimization,” IEEE Internet Things J
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Liang, Q. Chen, G. Zhu, H. Jiang, Y. C. Eldar, and S. Cui, “Communication-and-energy efficient over-the-air federated learning,” IEEE Trans. Wireless Commun
2024
Later among the works it cites.
H. Sun, H. Tian, W. Ni, J. Zheng, D. Niyato, and P. Zhang, “Federated low-rank adaptation for large models fine-tuning over wireless networks,” IEEE Trans. Wireless Commun
2024
Later among the works it cites.
P. Yang, D. Wen, Q. Zeng, Y. Zhou, T. Wang, H. Cai, and Y. Shi, “Over-the-air computation empowered vertically split inference,” IEEE Trans. Wireless Commun
2024
Later among the works it cites.
B. Tadych, “Distributed llama.” https://github.com/b4rtaz/distributed-llama , 2024
2024
Later among the works it cites.