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Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs).
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Q. Ho, J. Cipar, H. Cui, J. K. Kim, S. Lee, P. B. Gibbons, G. A. Gibson, G. R. Ganger, and E. P. Xing, “More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server,” in Proc. of the 27th NIPS , Dec. 2013, pp. 1223–1231
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J. Bernstein, Y.-X. Wang, K. Azizzadenesheli, and A. Anandkumar, “signSGD: Compressed Optimisation for Non-Convex Problems,” in Proc. of the 35th ICML , Jul. 2018, pp. 560–569
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J. Wu, W. Huang, J. Huang, and T. Zhang, “Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization,” in Proc. of the 35th ICML , Jul. 2018, pp. 5325–5333
2018
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2018
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A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language Models are Unsupervised Multitask Learners,” OpenAI blog , vol. 1, no. 8, p. 9, Feb. 2019
2019
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N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-Efficient Transfer Learning for NLP,” in Proc. of the 36th ICML , Jun. 2019, pp. 2790–2799
2019
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J. D. M.-W. C. Kenton and L. K. Toutanova, “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding,” in Proc. of 17th NAACL-HLT , Jun. 2019, pp. 4171–4186
2019
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P. Molchanov, A. Mallya, S. Tyree, I. Frosio, and J. Kautz, “Importance Estimation for Neural Network Pruning,” in 2019 IEEE/CVF Conference on CVPR , Jun. 2019, pp. 11 264–11 272
2019
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N. H. Tran, W. Bao, A. Zomaya, M. N. Nguyen, and C. S. Hong, “Federated Learning over Wireless Networks: Optimization Model Design and Analysis,” in IEEE INFOCOM 2019-IEEE conference on computer communications , Apr. 2019, pp. 1387–1395
2019
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2020
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C. Labrín and F. Urdinez, “Principal Component Analysis,” in R for political data science , Nov. 2020, pp. 375–393
2020
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Z. Nan, H. Guan, X. Shen, and C. Liao, “Deep NLP-Based Co-evolvement for Synthesizing Code Analysis from Natural Language,” in Proc. of the 30th CC , Feb. 2021, pp. 141–152
2021
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J. Pfeiffer, A. Kamath, A. Rücklé, K. Cho, and I. Gurevych, “AdapterFusion: Non-Destructive Task Composition for Transfer Learning,” in Proc. of the 16th EACL , Apr. 2021, pp. 487–503
2021
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R. Karimi Mahabadi, J. Henderson, and S. Ruder, “Compacter: Efficient Low-Rank Hypercomplex Adapter Layers,” Dec. 2021, pp. 1022–1035
2021
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Y.-L. Sung, V. Nair, and C. A. Raffel, “Training Neural Networks with Fixed Sparse Masks,” Dec. 2021, pp. 24 193–24 205
2021
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W. Wu, L. He, W. Lin, R. Mao, C. Maple, and S. Jarvis, “SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead,” IEEE Transactions on Computers , vol. 70, no. 5, pp. 655–668, May 2021
2021
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S. Horvath, S. Laskaridis, M. Almeida, I. Leontiadis, S. Venieris, and N. Lane, “FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout,” Proc. of the 35th NeurIPS , pp. 12 876–12 889, Dec. 2021
2021
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A. Aghajanyan, S. Gupta, and L. Zettlemoyer, “Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning,” in Proc. of 11th AACL-IJCNLP , Aug. 2021, pp. 7319–7328
2021
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C. Liang, S. Zuo, M. Chen, H. Jiang, X. Liu, P. He, T. Zhao, and W. Chen, “Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization,” in Proc. of the 11th AACL-IJCNLP , Aug. 2021, pp. 6524–6538
2021
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S. Wang, M. Lee, S. Hosseinalipour, R. Morabito, M. Chiang, and C. G. Brinton, “Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation,” in IEEE INFOCOM 2021-IEEE Conference on Computer Communications , May 2021, pp. 1–10
2021
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Z. Lin, L. Wang, J. Ding, B. Tan, and S. Jin, “Channel power gain estimation for terahertz vehicle-to-infrastructure networks,” IEEE Commun. Lett. , vol. 27, no. 1, pp. 155–159, 2022
2022
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Z. Lin, L. Wang, J. Ding, Y. Xu, and B. Tan, “Tracking and transmission design in terahertz v2i networks,” IEEE Transactions on Wireless Communications , vol. 22, no. 6, pp. 3586–3598, 2022
2022
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B. Yuan, Y. He, J. Davis, T. Zhang, T. Dao, B. Chen, P. S. Liang, C. Re, and C. Zhang, “Decentralized Training of Foundation Models in Heterogeneous Environments,” Dec. 2022, pp. 25 464–25 477
2022
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J. Shin, Y. Li, Y. Liu, and S.-J. Lee, “FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients,” in Proc. of the 20th MobiSys , Jun. 2022, pp. 436–449
2022
Cited alongside, same era.
X. Shuai, Y. Shen, S. Jiang, Z. Zhao, Z. Yan, and G. Xing, “BalanceFL: Addressing Class Imbalance in Long-Tail Federated Learning,” in 21st ACM/IEEE IPSN , May. 2022, pp. 271–284
2022
Cited alongside, same era.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-Rank Adaptation of Large Language Models,” in Proc. of the 10th ICLR , Jan. 2022
2022
Cited alongside, same era.
A. Reisizadeh, I. Tziotis, H. Hassani, A. Mokhtari, and R. Pedarsani, “Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity,” IEEE Journal on Selected Areas in Information Theory , vol. 3, no. 2, pp. 197–205, Jun. 2022
2022
Cited alongside, same era.
Z. Hu, L. Wang, Y. Lan, W. Xu, E.-P. Lim, L. Bing, X. Xu, S. Poria, and R. Lee, “LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models,” in Proc. of the 28th EMNLP , Dec. 2023, pp. 5254–5276
2023
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2023
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D. Cai, Y. Wu, S. Wang, F. X. Lin, and M. Xu, “Efficient Federated Learning for Modern NLP,” in Proc. of the 29th MobiCom , Oct. 2023, pp. 1–16
2023
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X. Liu, T. Pang, and C. Fan, “Federated prompting and chain-of-thought reasoning for improving llms answering,” in The 16th KSEM , Aug. 2023, pp. 3–11
2023
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X. Ouyang, Z. Xie, J. Zhou, G. Xing, and J. Huang, “ClusterFL: A Clustering-based Federated Learning System for Human Activity Recognition,” ACM Transactions on Sensor Networks , vol. 19, no. 1, pp. 1–32, Dec. 2022
2022
Cited alongside, same era.
Q. Zhang, S. Zuo, C. Liang, A. Bukharin, P. He, W. Chen, and T. Zhao, “PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance,” in Proc. of the 39th ICML , Jul. 2022, pp. 26 809–26 823
2022
Cited alongside, same era.
T. Dettmers, M. Lewis, S. Shleifer, and L. Zettlemoyer, “8-bit Optimizers via Block-wise Quantization,” Jan. 2022
2022
Cited alongside, same era.
B. Y. Lin, C. He, Z. Ze, H. Wang, Y. Hua, C. Dupuy, R. Gupta, M. Soltanolkotabi, X. Ren, and S. Avestimehr, “FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks,” in Findings of the Association for Computational Linguistics: NAACL 2022 , Jul. 2022, pp. 157–175
2022
Cited alongside, same era.
C. Li, X. Zeng, M. Zhang, and Z. Cao, “PyramidFL: A Fine-grained Client Selection Framework for Efficient Federated Learning,” in Proc. of the 28th MobiCom , Oct. 2022, pp. 158–171
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann et al. , “Palm: Scaling Language Modeling with Pathways,” Journal of Machine Learning Research , vol. 24, no. 240, pp. 1–113, Aug. 2023
2023
Cited alongside, same era.
T. Che, J. Liu, Y. Zhou, J. Ren, J. Zhou, V. Sheng, H. Dai, and D. Dou, “Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization,” in Proc. of the 28th EMNLP , Dec. 2023, pp. 7871–7888
2023
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T. Zhang, L. Gao, S. Lee, M. Zhang, and S. Avestimehr, “TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training,” in 2023 IEEE/CVF Conference on CVPR , 2023, pp. 5063–5072
2023
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S. Lyu, Z. Lin, G. Qu, X. Chen, X. Huang, and P. Li, “Optimal resource allocation for u-shaped parallel split learning,” in Proc. Globecom Wkshps , 2023, pp. 197–202
2023
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T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “QLoRA: Efficient Finetuning of Quantized LLMs,” Dec. 2023
2023
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(2023) “Stanford alpaca: An instruction-following llama model”. Available: https://github.com/tatsu-lab/ stanford_alpaca
2023
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E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, “GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers,” in Proc. of the 11th ICLR , May 2023
2023
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2023
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M. Wortsman, T. Dettmers, L. Zettlemoyer, A. Morcos, A. Farhadi, and L. Schmidt, “Stable and Low-precision Training for Large-Scale Vision-Language Models,” Dec. 2023
2023
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Z. Lin, G. Qu, X. Chen, and K. Huang, “Split learning in 6g edge networks,” IEEE Wirel. Commun. , 2024
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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. Mob. Comput. , 2024
2024
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M. Hu, J. Zhang, X. Wang, S. Liu, and Z. Lin, “Accelerating federated learning with model segmentation for edge networks,” IEEE Transactions on Green Communications and Networking , 2024
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G. Zhu, Y. Deng, X. Chen, H. Zhang, Y. Fang, and T. F. Wong, “Esfl: Efficient split federated learning over resource-constrained heterogeneous wireless devices,” IEEE Internet of Things Journal , 2024
2024
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