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Recently, large language models (LLMs) have achieved remarkable breakthroughs, revolutionizing the natural language processing domain and beyond.
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. NIPS , 2013
2013
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K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-scale Image Recognition,” in Proc. ICLR , 2015
2015
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Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
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J. D. Beshay, A. Francini, and R. Prakash, “On the Fidelity of Single-Machine Network Emulation in Linux,” in Proc. of the 23rd IEEE MASCOTS , 2015
2015
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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 , 2017
2017
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J. Novikova, O. Dušek, and V. Rieser, “The E2E Dataset: New Challenges For End-to-End Generation,” in Proc. SIGDIAL , 2017
2017
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2018
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B. Hanin, “Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients?” in Proc. NIPS , 2018
2018
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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. ICML , 2019
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. NAACL-HLT , 2019
2019
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M. Tan and Q. Le, “EfficientNet: Rethinking Model Scaling For Convolutional Neural Networks,” in Proc. ICML , 2019
2019
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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 , 2019
2019
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D. Blalock, J. J. Gonzalez Ortiz, J. Frankle, and J. Guttag, “What is the State of Neural Network Pruning?” in Proc. MLSys , 2020
2020
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X. Gu, K. Huang, J. Zhang, and L. Huang, “Fast Federated Learning in the Presence of Arbitrary Device Unavailability,” in Proc. NIPS , 2021
2021
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C. Yang, Q. Wang, M. Xu, Z. Chen, K. Bian, Y. Liu, and X. Liu, “Characterizing Impacts of Heterogeneity in Federated Learning Upon Large-scale Smartphone Data,” in Proc. WWW , 2021
2021
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2021
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C.-J. Jhang, C.-X. Xue, J.-M. Hung, F.-C. Chang, and M.-F. Chang, “Challenges and Trends of SRAM-Based Computing-in-Memory for AI Edge Devices,” IEEE Trans. Circuits Syst. I, Reg. Papers , vol. 68, no. 5, pp. 1773–1786, 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. NIPS , 2021
2021
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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 Trans. Parallel Distrib. Syst. , vol. 33, no. 1, pp. 88–100, May. 2021
2021
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2021
Earlier work this paper cites.
J. Pfeiffer, A. Kamath, A. Rücklé, K. Cho, and I. Gurevych, “AdapterFusion: Non-Destructive Task Composition for Transfer Learning,” in Proc. EACL , 2021
2021
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R. Karimi Mahabadi, J. Henderson, and S. Ruder, “Compacter: Efficient Low-Rank Hypercomplex Adapter Layers,” in Proc. NIPS , 2021
2021
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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. ICLR , 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,” in Proc. NIPS , 2022
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. MobiSys , 2022
2022
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L. Dong, Z. Yang, X. Cai, Y. Zhao, Q. Ma, and X. Miao, “WAVE: Edge-Device Cooperated Real-Time Object Detection for Open-Air Applications,” IEEE Trans. Mobile Comput. , vol. 22, no. 7, pp. 4347–4357, 2022
2022
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 , 2022
2022
Cited alongside, same era.
X. Ouyang, Z. Xie, J. Zhou, G. Xing, and J. Huang, “ClusterFL: A Clustering-based Federated Learning System for Human Activity Recognition,” ACM Trans. Sensor Netw. , vol. 19, no. 1, pp. 1–32, 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
M. Hu, J. Zhang, X. Wang, S. Liu, and Z. Lin, “Accelerating Federated Learning with Model Segmentation for Edge Networks,” IEEE Trans. Green Commun. Netw. , 2024
2024
Later among the works it cites.
Z. Wang, Z. Shen, Y. He, G. Sun, H. Wang, L. Lyu, and A. Li, “FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations,” in Proc. NIPS , 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. , 2024
2024
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Tian, X. Li, H. Zhang, C. Zhao, B. Li, X. Wang, and F.-Y. Wang, “VistaGPT: Generative Parallel Transformers for Vehicles with Intelligent Systems for Transport Automation,” IEEE Trans. Intell. Veh. , 2023
2023
Cited alongside, same era.
K. Panchal, S. Choudhary, N. Parikh, L. Zhang, and H. Guan, “Flow: Per-instance Personalized Federated Learning,” in Proc. NIPS , 2023
2023
Cited alongside, same era.
D. Cai, Y. Wu, S. Wang, F. X. Lin, and M. Xu, “Efficient Federated Learning for Modern NLP,” in Proc. MobiCom , 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. EMNLP , 2023
2023
Cited alongside, same era.
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,” IEEE Commun. Mag. , 2023
2023
Cited alongside, same era.
2024
Later among the works it cites.
N. Dhar, B. Deng, D. Lo, X. Wu, L. Zhao, and K. Suo, “An Empirical Analysis and Resource Footprint Study of Deploying Large Language Models on Edge Devices,” in Proc. ACM SE , 2024, pp. 69–76
2024
Later among the works it cites.
D. Xu, W. Yin, H. Zhang, X. Jin, Y. Zhang, S. Wei, M. Xu, and X. Liu, “EdgeLLM: Fast On-device LLM Inference with Speculative Decoding,” IEEE Trans. Mobile Comput. , 2024
2024
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P. Theodoropoulos, K. E. Nikolakakis, and D. Kalogerias, “Federated Learning under Restricted User Availability,” in Proc. ICASSP , 2024
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
2024
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T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “QLoRA: Efficient Finetuning of Quantized LLMs,” Proc. NIPS , 2024
2024
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2024
Later among the works it cites.
J. Zhang, S. Vahidian, M. Kuo, C. Li, R. Zhang, T. Yu, G. Wang, and Y. Chen, “Towards Building the FederatedGPT: Federated Instruction Tuning,” in Proc. ICASSP , 2024
2024
Later among the works it cites.
F. Wu, Z. Li, Y. Li, B. Ding, and J. Gao, “FedBiOT: LLM Local Fine-tuning in Federated Learning Without Full Model,” in Proc. ACM KDD , 2024
2024
Later among the works it cites.
Y. Wang, S. Si, D. Li, M. Lukasik, F. Yu, C.-J. Hsieh, I. S. Dhillon, and S. Kumar, “Two-stage LLM Fine-tuning with Less Specialization and More Generalization,” in Proc. ICML , 2024
2024
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S. Kotha, J. M. Springer, and A. Raghunathan, “Understanding Catastrophic Forgetting in Language Models Via Implicit Inference,” in Proc. ICLR , 2024
2024
Later among the works it cites.
B. Xu, X. Shu, H. Mei, Z. Bai, B. Fernando, M. Z. Shou, and J. Tang, “DoFIT: Domain-aware Federated Instruction Tuning With Alleviated Catastrophic Forgetting,” in Proc. NIPS , 2024
2024
Later among the works it cites.
Y. J. Cho, L. Liu, Z. Xu, A. Fahrezi, and G. Joshi, “Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models,” in Proc. EMNLP , 2024
2024
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2024
Later among the works it cites.
(2024) “NVIDIA Jetson Embedded Systems Developer Kits and Modules”. Available: https://www.nvidia.cn/autonomous-machines/embedded-systems/?section=jetsonTX2
2024
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S. Hayou, N. Ghosh, and B. Yu, “LoRA+: Efficient Low Rank Adaptation of Large Models,” in Proc. ICML , 2024
2024
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2024
Later among the works it cites.
D. J. Kopiczko, T. Blankevoort, and Y. M. Asano, “VeRA: Vector-based Random Matrix Adaptation,” in Proc. ICLR , 2024
2024
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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 Commun. Surv. Tutor. , 2025
2025
Closest in time.
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 Trans. Mobile Comput. , 2025
2025
Closest in time.