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Bo Li 0001
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Publications
- 2025
- [j220]Ye Liu
, Shan Chang
RoPe-Door: Toward Robust and Persistent Backdoor Data Poisoning Attacks in Federated Learning. IEEE Netw. 39(3): 302-310 (2025) - [c246]Xinglin Pan
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models. ASPLOS (1) 2025: 524-539 - [c243]Wenxiang Lin, Xinglin Pan, Shaohuai Shi, Xuan Wang, Bo Li, Xiaowen Chu:
Mast: Efficient Training of Mixture-of-Experts Transformers with Task Pipelining and Ordering. ICDCS 2025: 560-570 - [c240]Ne Wang, Wenxiang Lin, Lin Zhang, Shaohuai Shi, Ruiting Zhou, Bo Li:
SP-MoE: Expediting Mixture-of-Experts Training with Optimized Pipelining Planning. INFOCOM 2025: 1-10 - [i49]Xinglin Pan, Wenxiang Lin, Lin Zhang, Shaohuai Shi, Zhenheng Tang, Rui Wang, Bo Li, Xiaowen Chu:
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models. CoRR abs/2501.10714 (2025) - [i41]Zhenheng Tang, Zichen Tang, Junlin Huang, Xinglin Pan, Rudan Yan, Yuxin Wang, Amelie Chi Zhou, Shaohuai Shi, Xiaowen Chu, Bo Li:
DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization. CoRR abs/2502.11058 (2025) - 2024
- [c234]Shaohuai Shi
ScheMoE: An Extensible Mixture-of-Experts Distributed Training System with Tasks Scheduling. EuroSys 2024: 236-249 - [c230]Jing Peng
Sparse Gradient Communication with AlltoAll for Accelerating Distributed Deep Learning. ICPP 2024: 148-157 - [c227]Xinglin Pan, Wenxiang Lin, Shaohuai Shi
Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules. INFOCOM 2024: 1880-1889 - [i31]Xinglin Pan, Wenxiang Lin, Shaohuai Shi, Xiaowen Chu, Weinong Sun, Bo Li:
Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules. CoRR abs/2407.00599 (2024) - [i30]Zhenheng Tang, Xueze Kang, Yiming Yin, Xinglin Pan, Yuxin Wang, Xin He, Qiang Wang, Rongfei Zeng, Kaiyong Zhao, Shaohuai Shi, Amelie Chi Zhou, Bo Li, Bingsheng He, Xiaowen Chu:
FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression. CoRR abs/2410.12707 (2024) - 2023
- [j191]Lin Zhang
Scalable K-FAC Training for Deep Neural Networks With Distributed Preconditioning. IEEE Trans. Cloud Comput. 11(3): 2365-2378 (2023) - [j185]Zhenheng Tang
GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication. IEEE Trans. Parallel Distributed Syst. 34(3): 909-922 (2023) - [c217]Lin Zhang, Shaohuai Shi
DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining. ICDCS 2023: 142-153 - [c216]Lin Zhang, Longteng Zhang, Shaohuai Shi
Evaluation and Optimization of Gradient Compression for Distributed Deep Learning. ICDCS 2023: 361-371 - [c212]Lin Zhang, Shaohuai Shi, Bo Li:
Eva: Practical Second-order Optimization with Kronecker-vectorized Approximation. ICLR 2023 - [c209]Shaohuai Shi, Xinglin Pan, Xiaowen Chu, Bo Li:
PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining. INFOCOM 2023: 1-10 - [c207]Lin Zhang, Shaohuai Shi, Bo Li:
Accelerating Distributed K-FAC with Efficient Collective Communication and Scheduling. INFOCOM 2023: 1-10 - [i28]Lin Zhang, Shaohuai Shi, Xiaowen Chu, Wei Wang, Bo Li, Chengjian Liu:
Decoupling the All-Reduce Primitive for Accelerating Distributed Deep Learning. CoRR abs/2302.12445 (2023) - [i27]Lin Zhang, Longteng Zhang, Shaohuai Shi, Xiaowen Chu, Bo Li:
Evaluation and Optimization of Gradient Compression for Distributed Deep Learning. CoRR abs/2306.08881 (2023) - [i26]Lin Zhang, Shaohuai Shi, Bo Li:
Eva: A General Vectorized Approximation Framework for Second-order Optimization. CoRR abs/2308.02123 (2023) - [i25]Longteng Zhang, Lin Zhang, Shaohuai Shi, Xiaowen Chu, Bo Li:
LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning. CoRR abs/2308.03303 (2023) - 2022
- [i17]Lin Zhang, Shaohuai Shi, Wei Wang, Bo Li:
Scalable K-FAC Training for Deep Neural Networks with Distributed Preconditioning. CoRR abs/2206.15143 (2022) - 2021
- [j167]Shaohuai Shi, Zhenheng Tang, Xiaowen Chu
A Quantitative Survey of Communication Optimizations in Distributed Deep Learning. IEEE Netw. 35(3): 230-237 (2021) - [j163]Shaohuai Shi
MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning. IEEE Trans. Parallel Distributed Syst. 32(8): 1903-1917 (2021) - [c188]Shaohuai Shi, Lin Zhang, Bo Li:
Accelerating Distributed K-FAC with Smart Parallelism of Computing and Communication Tasks. ICDCS 2021: 550-560 - [c185]Shaohuai Shi, Xiaowen Chu, Bo Li:
Exploiting Simultaneous Communications to Accelerate Data Parallel Distributed Deep Learning. INFOCOM 2021: 1-10 - [i11]Shaohuai Shi, Lin Zhang, Bo Li:
Accelerating Distributed K-FAC with Smart Parallelism of Computing and Communication Tasks. CoRR abs/2107.06533 (2021) - 2020
- [c178]Shaohuai Shi
Communication-Efficient Distributed Deep Learning with Merged Gradient Sparsification on GPUs. INFOCOM 2020: 406-415 - [i10]Zhenheng Tang, Shaohuai Shi, Xiaowen Chu, Wei Wang, Bo Li:
Communication-Efficient Distributed Deep Learning: A Comprehensive Survey. CoRR abs/2003.06307 (2020) - [i9]Shaohuai Shi, Zhenheng Tang, Xiaowen Chu, Chengjian Liu, Wei Wang, Bo Li:
Communication-Efficient Distributed Deep Learning: Survey, Evaluation, and Challenges. CoRR abs/2005.13247 (2020) - 2019
- [c174]Shaohuai Shi
MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms. INFOCOM 2019: 172-180 - [i7]Shaohuai Shi, Xiaowen Chu, Bo Li:
MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning. CoRR abs/1912.09268 (2019) - 2018
- [c170]Shaohuai Shi
A DAG Model of Synchronous Stochastic Gradient Descent in Distributed Deep Learning. ICPADS 2018: 425-432 - [i6]Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li:
Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs. CoRR abs/1805.03812 (2018) - [i4]Shaohuai Shi, Xiaowen Chu, Bo Li:
MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms. CoRR abs/1811.11141 (2018)
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last updated on 2025-11-16 00:32 CET by the dblp team
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