About Me

I am Jieming Bian, a Ph.D. Candidate in Computer Engineering at the University of Florida, advised by Prof. Jie Xu. Before joining UF, I was a master student at Columbia University, major in Operations Research. My research focuses on Federated Learning, Parameter-Efficient Fine-Tuning (PEFT), and Large Foundation Models.

🔥 News

• 2025.09:  🎉🎉 Two papers are accepted by NeurIPS 2025.
• 2025.06:  🎉🎉 One paper is accepted by ICCV 2025.
• 2025.05:  🎉🎉 I will join Amazon Alexa AI as an Applied Scientist Intern this summer.

📚 Publications

Conference Papers

• Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
  Lei Wang (equal contribution), Jieming Bian (equal contribution), Letian Zhang, Jie Xu
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2025
  [PDF], [Code]

• FedEL: Federated Elastic Learning for Heterogeneous Devices
  Letian Zhang, Bo Chen, Jieming Bian, Lei Wang, Jie Xu
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2025
  [PDF], [Code]

• LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement
  Jieming Bian (equal contribution), Lei Wang (equal contribution), Letian Zhang, Jie Xu
  International Conference on Computer Vision (ICCV), 2025
  [PDF], [Code]

• Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
  Lei Wang (equal contribution), Jieming Bian (equal contribution), Letian Zhang, Chen Chen, Jie Xu
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
  [PDF], [Code]

• Adaptive User-Centric Entanglement Routing in Quantum Data Networks
  Lei Wang, Jieming Bian, Jie Xu
  IEEE International Conference on Distributed Computing Systems (ICDCS), 2024
  [PDF]

• CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
  Jieming Bian, Lei Wang, Shaolei Ren, Jie Xu
  ACM e-Energy, 2024 (Best Paper Nomination)
  [PDF]

• Federated Learning with Instance-Dependent Noisy Label
  Lei Wang, Jieming Bian, Jie Xu
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024
  [PDF]

• FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology
  Yuanzhe Peng, Jieming Bian, Jie Xu
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024
  [PDF]

• Client Clustering for Energy-Efficient Clustered Federated Learning in Wireless Networks
  Jieming Bian, Jie Xu
  ACM UbiComp Adjunct / ISWC, 2023
  [PDF]

• Federated Learning via Indirect Server-Client Communications
  Jieming Bian, Cong Shen, Jie Xu
  Annual Conference on Information Sciences and Systems (CISS), 2023
  [PDF]

• Mobility-Assisted Federated Learning for Vehicular Edge Computing
  Jieming Bian, Jie Xu
  Asilomar Conference on Signals, Systems, and Computers, 2023
  [PDF]

Journal Papers

• Indirect-Communication Federated Learning via Mobile Transporters
  Jieming Bian, Cong Shen, Mingzhe Chen, Jie Xu
  IEEE Transactions on Mobile Computing, vol. 24, no. 6, 2025
  [PDF]

• Accelerating Hybrid Federated Learning Convergence Under Partial Participation
  Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu
  IEEE Transactions on Signal Processing, vol. 72, pp. 3258–3271, 2024
  [PDF]

• Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying
  Jieming Bian, Jie Xu
  IEEE Transactions on Vehicular Technology, vol. 73, no. 7, 2024
  [PDF]

• On the Local Cache Update Rules in Streaming Federated Learning
  Heqiang Wang, Jieming Bian, Jie Xu
  IEEE Internet of Things Journal, vol. 11, no. 6, 2024
  [PDF]

• Hybrid Federated Learning for Multimodal IoT Systems
  Yuanzhe Peng, Yusen Wu, Jieming Bian, Jie Xu
  IEEE Internet of Things Journal, vol. 11, no. 21, 2024
  [PDF]

Preprints

• Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
  Jieming Bian, Lei Wang, Jie Xu
  [PDF]

• FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-the-World LoRA
  Jieming Bian (equal contribution), Lei Wang (equal contribution), Letian Zhang, Jie Xu
  [PDF]

• A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning
  Jieming Bian, Yuanzhe Peng, Lei Wang, Yin Huang, Jie Xu
  [PDF]

• Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms
  Yuanzhe Peng, Jieming Bian, Lei Wang, Yin Huang, Jie Xu
  [PDF]

📖 Educations

• 2021.01 - now, University of Florida, Ph.D. in Electrical and Computer Engineering.
• 2019.09 - 2020.12, Columbia University, M.S. in Operations Research.
• 2016.09 - 2019.05, University of Colorado Denver, B.A. in Econometrics and Quantitative Economics.

💻 Internships

• 2025.05 - 2025.08, Applied Scientist Intern, Amazon, Boston, MA.
• 2024.06 - 2024.08, NLP Data Scientist Intern, American Express, New York, NY.
• 2020.06 - 2020.09, Data Scientist Intern, Cygnus Education, Bryn Mawr, PA.

💬 Service

• Conference Reviewer:
  AAAI 2026, ICCV 2025, NeurIPS 2025/2024/2023/2022, ICML 2025/2024, CVPR 2025, AISTATS 2025, ICLR 2025/2024

• Journal Reviewer:
  IEEE Transactions on Cognitive Communications and Networking (TCCN),
  IEEE Transactions on Signal Processing (TSP),
  IEEE Transactions on Mobile Computing (TMC),
  IEEE Internet of Things Journal (IoT),
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS),
  IEEE Transactions on Green Communications and Networking (TGCN),
  IEEE Transactions on Parallel and Distributed Systems (TPDS),
  IEEE Wireless Communications Letters (WCL),
  IEEE Transactions on Network Science and Engineering (TNSE),
  IEEE Transactions on Machine Learning in Communications and Networking (TMLCN)