About Me

I am Jieming Bian, an Applied Scientist on the Sandstone team at Amazon. I obtained my Ph.D. degree in Computer Engineering from the University of Florida under the supervision of Prof. Jie Xu. Prior to that, I received my M.S. in Operations Research from Columbia University. My research focuses on Federated Learning, Parameter-Efficient Fine-Tuning (PEFT), and Large Foundation Models.

🔥 News

• 2026.08:  🎉🎉 One paper is accepted by EMNLP 2026.
• 2026.05:  🎉🎉 One paper is accepted by ICML 2026.
• 2026.05:  🎉🎉 I officially received my Ph.D. degree from the University of Florida and will join Amazon Alexa AI as a full-time Applied Scientist.
• 2026.04:  🎉🎉 I received the Graduate Student Excellence Award – Research from the Department of Electrical and Computer Engineering at the University of Florida.
• 2025.11:  🎉🎉 One paper is accepted by AAAI 2026.
• 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

• EMNLP Findings 2026 Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity
  Lei Wang (equal contribution), Jieming Bian (equal contribution), Letian Zhang, Jie Xu
  Conference on Empirical Methods in Natural Language Processing (EMNLP), Findings, 2026
  [PDF], [Code]

• ICML 2026 FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
  Jieming Bian (equal contribution), Lei Wang (equal contribution), Letian Zhang, Jie Xu
  International Conference on Machine Learning (ICML), 2026
  [PDF], [Code]

• AAAI 2026 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
  AAAI Conference on Artificial Intelligence (AAAI), 2026
  [PDF], [Code]

• NeurIPS 2025 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]

• NeurIPS 2025 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]

• ICCV 2025 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]

• NeurIPS 2024 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]

• ICDCS 2024 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]

• e-Energy 2024 CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
  Jieming Bian, Lei Wang, Shaolei Ren, Jie Xu
  The 15th ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy), 2024 (Best Paper Nomination)
  [PDF]

• ICASSP 2024 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]

• ICASSP 2024 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]

• UbiComp-CPD 2023 Client Clustering for Energy-Efficient Clustered Federated Learning in Wireless Networks
  Jieming Bian, Jie Xu
  UbiComp/ISWC’23 Workshop on Combining Physical and Data-Driven Knowledge in Ubiquitous Computing (UbiComp-CPD), 2023
  [PDF]

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

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

Journal Papers

• TMC 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]

• TSP 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]

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

• IoT-J 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]

• IoT-J 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]

• 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]

📖 Education

• 2024.08 - 2026.05, University of Florida, Ph.D. in Electrical and Computer Engineering.
• 2021.01 - 2024.08, University of Miami, 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.

💻 Experience

• 2026.10 - Present, Applied Scientist, Amazon (Sandstone), San Diego, CA.
• 2026.05 - 2026.10, Applied Scientist, Amazon (Alexa AI), Boston, MA.
• 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 2026/2027, ICCV 2025, NeurIPS 2026/2025/2024/2023/2022, ICML 2026/2025/2024, CVPR 2025, AISTATS 2026/2025, ICLR 2026/2025/2024, ARR 2026

• Journal Reviewer:
  IEEE TPAMI, TDSC, TCCN, TSP, TMC, IoT-J, TNNLS, TGCN, TPDS, WCL, TNSE, TMLCN