Fetching the paper…
Reading the bibliography…
Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, and J. Passerat-Palmbach, “A generic framework for privacy preserving deep learning,” 2018
2018
Earlier work this paper cites.
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, “Quantization and training of neural networks for efficient integer-arithmetic-only inference,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2704–2713
2018
Earlier work this paper cites.
TensorFlow Developers , “Tensorflow federated,” 2019. [Online]. Available: https://www.tensorflow.org/federated
2019
Earlier work this paper cites.
M. Feurer and F. Hutter, “Hyperparameter optimization,” Automated machine learning: Methods, systems, challenges , pp. 3–33, 2019
2019
Earlier work this paper cites.
Y. Xu, L. Ma, F. Yang, Y. Chen, K. Ma, J. Yang, X. Yang, Y. Chen, C. Shu, Z. Fan, J. Gan, X. Zou, R. Huang, C. Zhang, X. Liu, D. Tu, C. Xu, W. Zhang, D. Yang, M.-W. Wang, X. Wang, X. Xie, H. Leng, N. Holalkere, N. J. Halin, I. R. Kamel, J. Wu, X. Peng, X. Wang, J. Shao, P. Mongkolwat, J. Zhang, D. L. Rubin, G. Wang, C. Zheng, Z. Li, X. Bai, and T. Xia, “A collaborative online ai engine for ct-based covid-19 diagnosis,” medRxiv , 2020. [Online]. Available: https://www.medrxiv.org/content/early/2020/05/19/2020.05.10.20096073
2020
Earlier work this paper cites.
K. Tan, D. Bremner, J. L. Kernec, and M. Imran, “Federated machine learning in vehicular networks: A summary of recent applications,” in 2020 International Conference on UK-China Emerging Technologies (UCET) , 2020, pp. 1–4
2020
Earlier work this paper cites.
S. Niknam, H. S. Dhillon, and J. H. Reed, “Federated learning for wireless communications: Motivation, opportunities, and challenges,” IEEE Communications Magazine , vol. 58, no. 6, pp. 46–51, 2020
2020
Earlier work this paper cites.
M. M. Amiri and D. Gündüz, “Federated learning over wireless fading channels,” IEEE Transactions on Wireless Communications , vol. 19, no. 5, pp. 3546–3557, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
L. Floridi and M. Chiriatti, “Gpt-3: Its nature, scope, limits, and consequences,” Minds and Machines , vol. 30, pp. 681 – 694, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:228954221
2020
Cited alongside, same era.
A. Elgabli, J. Park, C. B. Issaid, and M. Bennis, “Harnessing wireless channels for scalable and privacy-preserving federated learning,” IEEE Transactions on Communications , vol. 69, no. 8, pp. 5194–5208, 2021
2021
Cited alongside, same era.
C. B. Issaid, A. Elgabli, J. Park, M. Bennis, and M. Debbah, “Communication efficient decentralized learning over bipartite graphs,” IEEE Transactions on Wireless Communications , vol. 21, no. 6, pp. 4150–4167, 2021
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Han, “Unsloth,” 2023. [Online]. Available: https://github.com/unslothai/unsloth.git
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
P. Ren, Y. Xiao, X. Chang, P.-y. Huang, Z. Li, X. Chen, and X. Wang, “A comprehensive survey of neural architecture search: Challenges and solutions,” ACM Comput. Surv. , vol. 54, no. 4, may 2021. [Online]. Available: https://doi.org/10.1145/3447582
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2023
Cited alongside, same era.
Q. Gu, “Llm-based code generation method for golang compiler testing,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE 2023. New York, NY, USA: Association for Computing Machinery, 2023, p. 2201–2203. [Online]. Available: https://doi.org/10.1145/3611643.3617850
2023
Cited alongside, same era.
J. Lin, K. Dzeparoska, A. Tizghadam, and A. Leon-Garcia, “Appleseed: Intent-based multi-domain infrastructure management via few-shot learning,” in 2023 IEEE 9th International Conference on Network Softwarization (NetSoft) , 2023, pp. 539–544
2023
Cited alongside, same era.
S. Kwon, S. Lee, T. Kim, D. Ryu, and J. Baik, “Exploring llm-based automated repairing of ansible script in edge-cloud infrastructures,” Journal of Web Engineering , vol. 22, no. 06, p. 889–912, Dec. 2023. [Online]. Available: https://journals.riverpublishers.com/index.php/JWE/article/view/24673
2023
Cited alongside, same era.
2023
Cited alongside, same era.
S. Thakur, B. Ahmad, H. Pearce, B. Tan, B. Dolan-Gavitt, R. Karri, and S. Garg, “Verigen: A large language model for verilog code generation,” ACM Trans. Des. Autom. Electron. Syst. , feb 2024, just Accepted. [Online]. Available: https://doi.org/10.1145/3643681
2024
Closest in time.
2024
Closest in time.
T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “Qlora: Efficient finetuning of quantized llms,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
React Developers , “React framework,” 2024. [Online]. Available: https://react.dev/
2024
Closest in time.
Redux Developers , “Redux framework,” 2024. [Online]. Available: https://redux.js.org/
2024
Closest in time.
A. Betlen, “llama.cpp binding for python,” 2024. [Online]. Available: https://github.com/abetlen/llama-cpp-python
2024
Closest in time.
G. Gerganov, “llama.cpp library,” 2024. [Online]. Available: https://github.com/ggerganov/llama.cpp
2024
Closest in time.