publications

My publications

Publications

The $\star$ represents equal contribution, 📧 corresponding author.

2026

  • Q. Li, J. Wu, X. Liu, Y. Wang, Z. Li, Y. Chen, S. Shi, Z. Tang📧, X. Chu📧. Reasoning Language Model Inference Serving Unveiled: An Empirical Study. In ICLR 2026.
  • Z. Tang$\star$, Z. Tang$\star$📧, G. Pan, B. Liu, K. Lai, X. Chu📧, B. Li📧. Ghost in the Cloud: Your Geo-Distributed Large Language Models Training is Easily Manipulated. In ICLR 2026.
  • F. Wei$\star$, Z. Tang$\star$, R. Zeng, T. Liu, C. Zhang, X. Chu, B. Han📧. JailbreakLoRA: Your Downloaded LoRA from Sharing Platforms might be Unsafe. In ICLR 2026.
  • Z. Tang$\star$, Z. Tang$\star$, J. Huang, X. Pan, R. Yan, Y. Wang, A. C. Zhou, S. Shi📧, X. Chu📧, B. Li📧. DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization. In MLSys 2026.

2025

  • X. Liu$\star$, Z. Tang$\star$, P. Dong, Z. Li, B. Li, X. Hu, X. Chu📧. ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference. In NeurIPS 2025.
  • Q. Wang, Z. Lou, Z. Tang, N. Chen, X. Zhao, W. Zhang, D. Song, B. He. Assessing Judging Bias in Large Reasoning Models: An Empirical Study. In COLM 2025.
  • Q. Wang, T. Wang, Z. Tang, Q. Li, N. Chen, J. Liang, B. He. MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs. In ACL 2025 Findings.
  • P. Dong$\star$, Z. Tang$\star$📧, X. Liu, L. Li, X. Chu📧, B. Li. Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression. In ICML 2025.
  • Y. Zhu$\star$, Z. Tang$\star$, X. Liu, A. Li, B. Li, X. Chu, B. Han📧. OracleKV: Oracle Guidance for Question-Independent KV Cache Eviction. ICML 2025 Workshop LCFM (Oral).
  • Q. Wang$\star$, Z. Tang$\star$, Z. Jiang, N. Chen, T. Wang, B. He. AgentTaxo: Dissecting and Benchmarking Token Distribution of LLM Multi-Agent Systems. ICML 2025 Workshop MAS.
  • P. Dong, L. Li, Z. Tang, X. Liu, Z. Wei, Q. Wang, X. Chu📧. ParZC: Parametric Zero-Cost Proxies for Efficient NAS. In AAAI 2025 (Oral Presentation).
  • X. Pan, W. Lin, L. Zhang, S. Shi, Z. Tang, R. Wang, B. Li, X. Chu📧. FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models. In ASPLOS 2025.
  • L. Shen$\star$, Z. Tang$\star$, L. Wu, Y. Zhang, X. Chu, T. Qin, B. Han📧. Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection. In ICLR 2025.
  • Z. Tang, X. Liu, Q. Wang, P. Dong, B. He, X. Chu📧, B. Li📧. The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve? ICLR 2025 Blogpost.
  • Q. Wang, Z. Tang, B. He. Can LLM Simulations Truly Reflect Humanity? A Deep Dive. ICLR 2025 Blogpost.
  • P. Dong, L. Li, Y. Zhong, D. Du, R. Fan, Y. Chen, Z. Tang, Q. Wang, W. Xue, Y. Guo, X. Chu📧. STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs. In ICLR 2025.
  • X. Liu, Z. Tang, H. Chen, P. Dong, Z. Li, X. Zhou, B. Li, X. Hu, X. Chu📧. Can LLMs Maintain Fundamental Abilities under KV Cache Compression? Arxiv 2025.
  • K. Lai$\star$, Z. Tang$\star$, X. Pan, P. Dong, X. Liu, H. Chen, L. Shen, B. Li, X. Chu📧. Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing. Arxiv 2025.

2024

  • Z. Tang, Y. Zhang, P. Dong, Y. Cheung, A. C. Zhou, B. Han, X. Chu📧. FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. In NeurIPS 2024 (Spotlight).
  • L. Shen$\star$, Z. Tang$\star$💡, L. Wu, Y. Zhang, X. Chu, T. Qin, B. Han📧. Hot Pluggable Federated Learning. Federated Foundation Models@NeurIPS 2024 Workshop (Oral, Outstanding Student Paper Award).
  • L. Li, P. Dong, Z. Tang, X. Liu, Q. Wang, W. Luo, W. Xue, Q. Liu, X. Chu📧, Y. Guo. Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models. In NeurIPS 2024.
  • Q. Li, X. Liu, Z. Tang, P. Dong, Z. Li, X. Pan, X. Chu📧. Should We Really Edit Language Models? On the Evaluation of Edited Language Models. In NeurIPS 2024.
  • P. Dong, L. Li, X. Liu, Z. Tang, X. Liu, Q. Wang, X. Chu📧. LPZero: Language Model Zero-cost Proxy Search from Zero. In EMNLP 2024 Findings.
  • Z. Tang, J. Huang, R. Yan, Y. Wang, Z. Tang💡📧, S. Shi, A. C. Zhou, X. Chu📧. Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning. In ICPP 2024.
  • P. Dong, L. Li, Z. Tang, X. Liu, X. Pan, Q. Wang, X. Chu📧. Evolving Symbolic Pruning Metric From Scratch for Large Language Models. In ICML 2024.
  • Y. Tang, P. Dong, Z. Tang, X. Chu, J. Liang📧. VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting. In CVPR 2024 Workshop.
  • Z. Tang, Y. Zhang, S. Shi, X. Tian, T. Liu, B. Han, X. Chu📧. FedImpro: Measuring and Improving Client Update in Federated Learning. In ICLR 2024.
  • Y. Wang, Y. Chen, Z. Li, Z. Tang, R. Guo, X. Wang, Q. Wang, A. C. Zhou, X. Chu📧. BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems. arXiv:2401.17644.
  • Y. Wang, S. Shi, X. He, Z. Tang, X. Pan, Y. Zheng, X. Wu, A. C. Zhou, B. He, X. Chu📧. Reliable and Efficient In-Memory Fault Tolerance of Large Language Model Pretraining. arXiv:2310.12670.

2023

  • Z. Tang, Y. Wang, X. He, L. Zhang, X. Pan, Q. Wang, R. Zeng, K. Zhao, S. Shi📧, B. He, X. Chu📧. FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs. In IJCAI-LLM 2023.
  • X. He, J. Yao, Y. Wang, Z. Tang, C. K. Chun, S. Simon, B. Han, X. Chu📧. NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension. In AAAI 2023.

2022

  • Z. Tang, S. Shi, B. Li, X. Chu📧. GossipFL: A Decentralized Federated Learning Framework with Sparsified and Adaptive Communication. In IEEE TPDS 2022.
  • Z. Tang, Y. Zhang$\star$, S. Shi, X. He, B. Han, X. Chu📧. Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. In ICML 2022.
  • C. He, A. D. Shah, Zhenheng Tang, D. Fan, A. N. Sivashunmugam, K. Bhogaraju, M. Shimpi, L. Shen, X. Chu, M. Soltanolkotabi, S. Avestimehr. FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks. In FL-AAAI 2022 (Best Paper Award).

2021

  • Z. Liao, H. Yan, Z. Tang, X. Chu, T. Tao📧. Deep learning identifies leak in water pipeline system using transient frequency response. Process Safety and Environmental Protection 2021.
  • Z. Tang, Zhikai Hu, Shaohuai Shi, Yiu-ming Cheung, Yilun Jin, Zhenghang Ren, Xiaowen Chu📧. Data Resampling for Federated Learning with Non-IID Labels. FTL-IJCAI Workshop 2021.
  • S. Shi, Z. Tang, X. Chu, C. Liu, W. Wang, B. Li. A quantitative survey of communication optimizations in distributed deep learning. IEEE Network 2021.

2020 and earlier

  • S. Shi, Z. Tang, Q. Wang, K. Zhao, X. Chu. Layer-wise adaptive gradient sparsification for distributed deep learning with convergence guarantees. ECAI 2020.
  • Z. Tang, S. Shi, X. Chu📧. Communication-efficient decentralized learning with sparsification and adaptive peer selection. ICDCS 2020.
  • Y. Wang, Q. Wang, S. Shi, X. He, Z. Tang, K. Zhao, X. Chu. Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training. CCGRID 2020.
  • Z. Tang, Y. Wang, Q. Wang, X. Chu. The impact of GPU DVFS on the energy and performance of deep learning: An empirical study. ACM e-Energy 2019.
  • S. Shi, K. Zhao, Q. Wang, Z. Tang, X. Chu. A convergence analysis of distributed SGD with communication-efficient gradient sparsification. IJCAI 2019.
  • S. Shi, Q. Wang, K. Zhao, Z. Tang, Y. Wang, X. Huang, X. Chu. A distributed synchronous SGD algorithm with global top-k sparsification for low bandwidth networks. ICDCS 2019.
  • X. Zhou, Z. Tang, W. Xu, F. Meng, X. Chu, K. Xin, G. Fu📧. Deep learning identifies accurate burst locations in water distribution networks. Water Research 2019.
  • X. He, S. Wang, S. Shi, Z. Tang, Y. Wang, Z. Zhao, J. Dai, R. Ni, X. Zhang, X. Liu, Z. Wu, W. Yu, X. Chu. Computer-aided clinical skin disease diagnosis using CNN and object detection models. IEEE BigData 2019.

Preprints

  • Z. Tang, X. Chu, R. Ran, S. Lee, S. Shi, Y. Zhang, Y. Wang, A. Liang, S. Avestimehr, C. He📧. FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training. arXiv:2303.01778.
  • Z. Tang, S. Shi, W. Wang, B. Li, X. Chu📧. Communication-efficient distributed deep learning: A comprehensive survey. CoRR:2003.06307.

For a complete list, see my Google Scholar or DBLP.