| Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives T Shen, J Zhang, X Jia, F Zhang, Z Lv, K Kuang, C Wu, F Wu Frontiers of Information Technology & Electronic Engineering 24 (10), 1390-1402, 2023 | 121* | 2023 |
| Federated learning with label distribution skew via logits calibration J Zhang, Z Li, B Li, J Xu, S Wu, S Ding, C Wu International Conference on Machine Learning, 26311-26329, 2022 | 100 | 2022 |
| Dense: Data-free one-shot federated learning J Zhang, C Chen, B Li, L Lyu, S Wu, S Ding, C Shen, C Wu Advances in Neural Information Processing Systems 35, 21414-21428, 2022 | 86* | 2022 |
| Towards efficient data free black-box adversarial attack J Zhang, B Li, J Xu, S Wu, S Ding, L Zhang, C Wu CVPR 2022, 15115-15125, 2022 | 56 | 2022 |
| Accelerating Dataset Distillation via Model Augmentation L Zhang, J Zhang, B Lei, S Mukherjee, X Pan, B Zhao, C Ding, Y Li, D Xu CVPR 2023, 2022 | 40 | 2022 |
| Gear: a margin-based federated adversarial training approach C Chen, J Zhang, L Lyu International Workshop on Trustable, Verifiable, and Auditable Federated …, 2022 | 27* | 2022 |
| Target: Federated class-continual learning via exemplar-free distillation J Zhang, C Chen, W Zhuang, L Lyu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 24* | 2023 |
| Delving into the adversarial robustness of federated learning J Zhang, B Li, C Chen, L Lyu, S Wu, S Ding, C Wu AAAI 2023, 2023 | 19 | 2023 |
| Ideal: Query-efficient data-free learning from black-box models J Zhang, C Chen, L Lyu The Eleventh International Conference on Learning Representations, 2022 | 18* | 2022 |
| Sampling to distill: Knowledge transfer from open-world data Y Wang, Z Chen, J Zhang, D Yang, Z Ge, Y Liu, S Liu, Y Sun, W Zhang, ... ACM MM 2024, 2023 | 10 | 2023 |
| Adversarial examples for good: Adversarial examples guided imbalanced learning J Zhang, L Zhang, G Li, C Wu 2022 IEEE International Conference on Image Processing (ICIP), 136-140, 2022 | 10 | 2022 |
| Federated generative learning with foundation models J Zhang, X Qi, B Zhao arXiv preprint arXiv:2306.16064, 2023 | 9 | 2023 |
| Real-fake: Effective training data synthesis through distribution matching J Yuan, J Zhang, S Sun, P Torr, B Zhao International Conference on Learning Representations (ICLR), 2024 | 7 | 2024 |
| Rethinking Data Distillation: Do Not Overlook Calibration D Zhu, B Lei, J Zhang, Y Fang, R Zhang, Y Xie, D Xu ICCV 2023, 2023 | 7 | 2023 |
| Feddtg: Federated data-free knowledge distillation via three-player generative adversarial networks Z Zhang, T Shen, J Zhang, C Wu arXiv preprint arXiv:2201.03169, 2022 | 7 | 2022 |
| Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models J Ma, A Cao, Z Xiao, J Zhang, C Ye, J Zhao arXiv preprint arXiv:2404.02928, 2024 | 4 | 2024 |
| Diffclass: Diffusion-based class incremental learning Z Meng, J Zhang, C Yang, Z Zhan, P Zhao, Y WAng ECCV 2024, 2024 | 4 | 2024 |
| AgentDojo: A Dynamic Environment to Evaluate Attacks and Defenses for LLM Agents E Debenedetti, J Zhang, M Balunović, L Beurer-Kellner, M Fischer, ... arXiv preprint arXiv:2406.13352, 2024 | 1 | 2024 |
| Evaluations of Machine Learning Privacy Defenses are Misleading M Aerni, J Zhang, F Tramèr CCS 2024, 2024 | 1 | 2024 |
| Federated Domain Adaptation via Pseudo-label Refinement G Li, Q Zhang, P Wang, J Zhang, C Wu 2023 IEEE International Conference on Multimedia and Expo (ICME), 1829-1834, 2023 | 1 | 2023 |
