Jingwei Sun

发表

Hermes: an efficient federated learning framework for heterogeneous mobile clients

H. Li, Yiran Chen, Ang Li, 2021, MobiCom.

More Generalized and Personalized Unsupervised Representation Learning In A Distributed System

Yiran Chen, H. Li, Ang Li, 2022, ArXiv.

Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

Yiran Chen, H. Li, Chang Chen, 2022, ArXiv.

Soteria: Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective pdf

Jingwei Sun, Ang Li, Binghui Wang, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Next Generation Federated Learning for Edge Devices: An Overview

Hai Helen Li, Yiran Chen, Ang Li, 2022, 2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC).

LotteryFL: Empower Edge Intelligence with Personalized and Communication-Efficient Federated Learning

H. Li, Sicheng Li, Binghui Wang, 2021, IFIP International Information Security Conference.

FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking

Hai Li, Mi Zhang, Jingwei Sun, 2021, SenSys.

LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets pdf

Jingwei Sun, Hai Li, Ang Li, 2020, ArXiv.

FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective pdf

Amin Hassanzadeh, Hai Li, Louis DiValentin, 2021, NeurIPS.

FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

Yiran Chen, H. Li, Yu Wang, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

PrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Information

M. Gorlatova, Jingwei Sun, Lin Duan, 2023, ArXiv.

Rethinking normalization methods in federated learning

H. Li, Ang Li, Jingwei Sun, 2022, Proceedings of the 3rd International Workshop on Distributed Machine Learning.

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

H. Li, Yiran Chen, M. Gorlatova, 2022, SenSys.

SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models

Qilin Zheng, Ang Li, Jingwei Sun, 2023, ArXiv.

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Ziyue Xu, H. Roth, Daguang Xu, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking

H. Li, Mi Zhang, Xiao Zeng, 2021, SenSys.

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

Jingwei Sun, Zhixu Du, Yiran Chen, 2024, 2405.19686.

Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents

Jingwei Sun, Saeed Vahidian, N. Gong, 2023, ArXiv.

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

Daguang Xu, Dong Yang, Jingwei Sun, 2023, ArXiv.

LotteryFL: Empower Edge Intelligence with Personalized and Communication-Efficient Federated Learning

H. Li, Sicheng Li, Binghui Wang, 2021, 2021 IEEE/ACM Symposium on Edge Computing (SEC).