Jingwei (Jared) Sun

sun DOT jingwei AT ufl DOT edu

Gainesville, FL, USA

I am an assistant professor in the Department of Computer & Information Science & Engineering at the University of Florida. I got my Ph.D. degree in the Electrical and Computer Engineering department at Duke University under the supervision of Professor Yiran Chen and Hai “Helen” Li in Duke CEI Lab. My research interests lie in AI models and systems with a focus on foundation model intelligence scaling, AI model/agent inference and adaptation with an awareness of efficiency, personalization, and robustness.

Openings: I’m always looking for highly motivated Ph.D., master students, and research interns to join my group. If you are interested, please send me an email with your CV and transcripts attached.

My main research interests:

Foundation Models & Agents
– Coding agent
– VLM
– VLA
– Diffusion model
Efficient Inference and Adaptation
– LLM on the edge
– MoE
– Backpropagation-free Prompt Tuning on Edge Devices
Collaborative AI
– Edge-cloud Collaborative Inference
– Cross-silo Collaborative Inference
– Heterogeneous Federated Learning
Trustworthy AI
– Data Privacy Preservation
– Robust Collaborative Intelligence System
– Training Data Detection for LLMs

selected publications

preprint

LatentGate: Policy-State Filtering for Token-Efficient Verification of Software Engineering Agents

Yuning Han, Yangchenchen Jin, Tyler Jandreau, and 1 more author

Preprint, 2026

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preprint

CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration

Yuning Han, Yangchenchen Jin, Dylan Zhao, and 1 more author

arXiv preprint arXiv:2605.11186, 2026

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preprint

Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference

Yudong Liu, Yuan Li, Zijia Tang, and 12 more authors

arXiv preprint arXiv:2605.02739, 2026

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ACM MM

Zeus: Accelerating Diffusion Models with Only Second-Order Predictor

Oral

Yixiao Wang, Ting Jiang, Zishan Shao, and 6 more authors

ACM Multimedia, 2026

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ICLR

Min-k%++: Improved baseline for detecting pre-training data from large language models

Spotlight

Jingyang Zhang*, Jingwei Sun*, Eric Yeats, and 5 more authors

The Thirteenth International Conference on Learning Representations, 2025

Bib PDF Website

@article{zhang2024min,
  title = {Min-k\%++: Improved baseline for detecting pre-training data from large language models},
  author = {Zhang*, Jingyang and Sun*, Jingwei and Yeats, Eric and Ouyang, Yang and Kuo, Martin and Zhang, Jianyi and Yang, Hao Frank and Li, Hai},
  journal = {The Thirteenth International Conference on Learning Representations},
  award = {Spotlight},
  year = {2025},
}

preprint

Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback

Jingwei Sun, Zhixu Du, and Yiran Chen

arXiv preprint arXiv:2405.19686, 2024

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ICML

FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models

Best Paper Award in Federated Learning on the Edge, 2024 AAAI Spring Series Symposium

Jingwei Sun, Ziyue Xu, Hongxu Yin, and 6 more authors

Proceedings of the 41th International Conference on Machine Learning, 2024

Bib PDF Website

@article{sun2023fedbpt,
  title = {FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models},
  author = {Sun, Jingwei and Xu, Ziyue and Yin, Hongxu and Yang, Dong and Xu, Daguang and Liu, Yudong and Du, Zhixu and Chen, Yiran and Roth, Holger R},
  journal = {Proceedings of the 41th International Conference on Machine Learning},
  year = {2024},
  award = {Best Paper Award in Federated Learning on the Edge, 2024 AAAI Spring Series Symposium},
}

NeurIPS

Reimagining Mutual Information for Enhanced Defense against Data Leakage in Collaborative Inference

Lin Duan*, Jingwei Sun*, Yiran Chen, and 1 more author

NeurIPS, 2024

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ICCV

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Jingwei Sun, Ziyue Xu, Dong Yang, and 6 more authors

2023

Bib PDF

@article{sun2023communication,
  title = {Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples},
  author = {Sun, Jingwei and Xu, Ziyue and Yang, Dong and Nath, Vishwesh and Li, Wenqi and Zhao, Can and Xu, Daguang and Chen, Yiran and Roth, Holger R},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year = {2023},
}

SenSys
FedSEA: A Semi-Asynchronous Federated Learning Framework for Extremely Heterogeneous Devices

Jingwei Sun, Ang Li, Lin Duan, and 8 more authors

2022

Abs Bib HTML PDF

Federated learning (FL) has attracted increasing attention as a promising technique to drive a vast number of edge devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of a FL system in practice due to the heterogeneous computation resources on different devices. To improve the efficiency of FL systems in the real world, asynchronous FL (AFL) and semi-asynchronous FL (SAFL) methods are proposed such that the server does not need to wait for stragglers. However, existing AFL and SAFL systems suffer from poor accuracy and low efficiency in realistic settings where the data is non-IID distributed across devices and the on-device resources are extremely heterogeneous. In this work, we propose FedSEA - a semi-asynchronous FL framework for extremely heterogeneous devices. We theoretically disclose that the unbalanced aggregation frequency is a root cause of accuracy drop in SAFL. Based on this analysis, we design a training configuration scheduler to balance the aggregation frequency of devices such that the accuracy can be improved. To improve the efficiency of the system in realistic settings where the devices have dynamic on-device resource availability, we design a scheduler that can efficiently predict the arriving time of local updates from devices and adjust the synchronization time point according to the devices’ predicted arriving time. We also consider the extremely heterogeneous settings where there exist extremely lagging devices that take hundreds of times as long as the training time of the other devices. In the real world, there might be even some extreme stragglers which are not capable of training the global model. To enable these devices to join in training without impairing the systematic efficiency, Fed-SEA enables these extreme stragglers to conduct local training on much smaller models. Our experiments show that compared with status quo approaches, FedSEA improves the inference accuracy by 44.34% and reduces the systematic time cost and local training time cost by 87.02X and 792.9X. FedSEA also reduces the energy consumption of the devices with extremely limited resources by 752.9X.
@article{sun2022fedsea, title = {FedSEA: A Semi-Asynchronous Federated Learning Framework for Extremely Heterogeneous Devices}, author = {Sun, Jingwei and Li, Ang and Duan, Lin and Alam, Samiul and Deng, Xuliang and Guo, Xin and Wang, Haiming and Gorlatova, Maria and Zhang, Mi and Li, Hai and others}, booktitle = {Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems}, year = {2022}, }

CVPR

Soteria: Provable defense against privacy leakage in federated learning from representation perspective

Jingwei Sun, Ang Li, Binghui Wang, and 3 more authors

In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021

Bib PDF Code

@inproceedings{sun2021soteria,
  title = {Soteria: Provable defense against privacy leakage in federated learning from representation perspective},
  author = {Sun, Jingwei and Li, Ang and Wang, Binghui and Yang, Huanrui and Li, Hai and Chen, Yiran},
  booktitle = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages = {9311--9319},
  year = {2021},
}

NeurIPS

Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective

Jingwei Sun, Ang Li, Louis DiValentin, and 3 more authors

Advances in Neural Information Processing Systems, 2021

Bib PDF Code

@article{sun2021fl,
  title = {Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective},
  author = {Sun, Jingwei and Li, Ang and DiValentin, Louis and Hassanzadeh, Amin and Chen, Yiran and Li, Hai},
  journal = {Advances in Neural Information Processing Systems},
  volume = {34},
  pages = {12613--12624},
  year = {2021},
}