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Chen Chen (谌晨) I am a research scientist at Sony AI in the Privacy-Preserving Machine Learning team lead by Lingjuan Lyu. Prior to that, I reveived my Ph.D. degree from Zhejiang University in 2022. I am interested in trustworthy machine learning (privacy, security, etc). My current research focuses on federated learning and adversarial training.
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Education
Ph.D. in computer science and technology, Zhejiang University, China (Sep. 2017 - Dec. 2022)
B.E. (honor) in computer science and technology, Chu Kochen Honors College (About 300 best students out of 6000 freshmen are chosen), Zhejiang University, China (Sep. 2013 - Jun. 2017)
Advisor: Professor Gang Chen
Publications
(* indicates equal contributions)
How to Trace Latent Generative Model Generated Images without Artificial Watermark?
Zhenting Wang, Vikash Sehwag, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma.
The Forty-First International Conference on Machine Learning (ICML 2024 ) .
COALA: A Practical and Vision-Centric Federated Learning Platform.
Weiming Zhuang, Jian Xu, Chen Chen, Jingtao Li, Lingjuan Lyu.
The Forty-First International Conference on Machine Learning (ICML 2024 ) .
A Simple Background Augmentation Method for Object Detection with Diffusion Model.
Yuhang Li, Xin Dong, Chen Chen, Weiming Zhuang, Lingjuan Lyu.
The European Conference on Computer Vision (ECCV 2024 ) .
FedMef: Towards Memory-efficient Federated Dynamic Pruning.
Hong Huang, Weiming Zhuang, Chen Chen, Lingjuan Lyu.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024 ).
Detecting, Explaining, and Mitigating Memorization in Diffusion Models.
Yuxin Wen, Yuchen Liu, Chen Chen, Lingjuan Lyu.
The Twelfth International Conference on Learning Representations (ICLR 2024 ) Oral .
DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models.
Zhenting Wang, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma.
The Twelfth International Conference on Learning Representations (ICLR 2024).
Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning.
Yue Tan, Chen Chen, Weiming Zhuang, Xin Dong, Lingjuan Lyu, Guodong Long.
The Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS 2023).
Where Did I Come From? Origin Attribution of AI-Generated Images.
Zhenting Wang, Chen Chen, Yi Zeng, Lingjuan Lyu, Shiqing Ma.
The Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS 2023).
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation.
Jie Zhang, Chen Chen, Weiming Zhuang, Lingjuan Lyu.
International Conference on Computer Vision 2023 (ICCV 2023).
Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting.
Yuchen Liu*, Chen Chen*, Lingjuan Lyu, Fangzhao Wu, Sai Wu, Gang Chen.
The Fortieth International Conference on Machine Learning (ICML 2023).
Towards Adversarially Robust Continual Learning.
Tao Bai, Chen Chen, Lingjuan Lyu, Jun Zhao, Bihan Wen.
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023).
IDEAL: Query-Efficient Data-Free Learning from Black-Box Models.
Jie Zhang*, Chen Chen*, Lingjuan Lyu.
The Eleventh International Conference on Learning Representations (ICLR 2023).
Delving into the Adversarial Robustness of Federated Learning.
Jie Zhang, Bo Li, Chen Chen, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chao Wu.
The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023).
Extracted BERT Model Leaks More Information than You Think!
Xuanli He, Chen Chen, Lingjuan Lyu, Qiongkai Xu.
The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) Oral .
CalFAT: Calibrated Federated Adversarial Training with Label Skewness.
Chen Chen, Yuchen Liu, Xingjun Ma, Lingjuan Lyu.
The Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022).
International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022 (FL-AAAI 2022) Best Student Paper.
DENSE: Data-Free One-Shot Federated Learning.
Jie Zhang*, Chen Chen*, Bo Li, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chunhua Shen, Chao Wu.
The Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022).
Privacy and Robustness in Federated Learning: Attacks and Defenses.
Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2022).
Byzantine-resilient Federated Learning via Gradient Memorization.
Chen Chen, Lingjuan Lyu, Yuchen Liu, Fangzhao Wu, Chaochao Chen and Gang Chen.
International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022 (FL-AAAI 2022).
Decision Boundary-aware Data Augmentation for Adversarial Training.
Chen Chen*, Jingfeng Zhang*, Xilie Xu, Lingjuan Lyu, Chaochao Chen, Tianlei Hu, and Gang Chen.
IEEE Transactions on Dependable and Secure Computing (TDSC 2022).
Practical Attribute Reconstruction Attack Against Federated Learning.
Chen Chen, Lingjuan Lyu, Han Yu, Gang Chen.
IEEE Transactions on Big Data (TBD 2022).
Online Partial Label Learning.
Haobo Wang, Yuzhou Qiang, Chen Chen, Weiwei Liu, Tianlei Hu, Zhao Li, Gang Chen.
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML 2020).
Incorporating label embedding and feature augmentation for multi-dimensional classification.
Haobo Wang*, Chen Chen*, Weiwei Liu, Ke Chen, Tianlei Hu, Gang Chen.
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).
Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification.
Chen Chen*, Haobo Wang*, Weiwei Liu, Xingyuan Zhao, Tianlei Hu, Gang Chen.
The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019).
CAMO: A Collaborative Ranking Method for Content Based Recommendation.
Chengwei Wang, Tengfei Zhou, Chen Chen, Tianlei Hu, Gang Chen.
The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019).
Experience
Research intern in Sony AI, Japan (Sep. 2021 — Dec. 2022)
Focused on privacy and robustness in federated learning. Lead a project that focuses on robustness (against adversarial attack) in federated learning. Specifically, the project considered federated adversarial training under the challenging non-IID setting.
Visiting Ph.D. in National University of Singapore, Singapore (Nov. 2019 — Nov. 2020)
Focused on robust federated learning, which can defend against Byzantine attacks and improve performance in federated learning.
Software engineering intern in NetEase, China (Jun. 2016 — Jun. 2019)
Took part in game development and developed a retargeting tool with C++ and a super-resolution tool with Python.