[1] |
MCMAHAN H B, MOORE E, RAMAGE D, et al. Communication-Efficient Learning of Deep Networks from Decentralized Data[EB/OL]. (2023-01-26)[2024-05-23]. https://arxiv.org/abs/1602.05629v4.
|
[2] |
LANSARI M, BELLAFQIRA R, KAPUSTA K, et al. When Federated Learning Meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection[J]. Machine Learning and Knowledge Extraction, 2023, 5(4): 1382-1406.
|
[3] |
YANG Qiang, LIU Yang, CHEN Tianjian, et al. Federated Machine Learning[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 1-19.
|
[4] |
LI Tian, SAHU A K, TALWALKAR A, et al. Federated Learning: Challenges, Methods, and Future Directions[J]. IEEE Signal Processing Magazine, 2020, 37(3): 50-60.
doi: 10.1109/MSP.2020.2975749
|
[5] |
BISHOP C M. Pattern Recognition and Machine Learning[M]. New York: Springer, 2006.
|
[6] |
KIM B, LEE S, LEE S, et al. Margin-Based Neural Network Watermarking[C]// PMLR. International Conference on Machine Learning. New York: PMLR, 2023: 16696-16711.
|
[7] |
PENG Zirui, LI Shaofeng, CHEN Guoxing, et al. Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations[C]// IEEE. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2022: 13420-13429.
|
[8] |
TIAN Yulong, SUYA F, XU Fengyuan, et al. Stealthy Backdoors as Compression Artifacts[J]. IEEE Transactions on Information Forensics and Security, 2022, 17: 1372-1387.
|
[9] |
LI Yiming, WU Baoyuan, JIANG Yong, et al. Backdoor Learning: A Survey[EB/OL]. (2020-08-21)[2024-05-23]. https://arxiv.org/abs/2007.08745v2.
|
[10] |
ADI Y, BAUM C, CISSE M, et al. Turning Your Weakness into a Strength: Watermarking Deep Neural Networks by Backdooring[EB/OL]. (2018-06-11)[2024-05-23]. https://arxiv.org/abs/1802.04633v3.
|
[11] |
CHEN Jinyin, LI Mingjun, CHENG Yao, et al. FedRight: An Effective Model Copyright Protection for Federated Learning[EB/OL]. (2023-09-26)[2024-05-23]. https://doi.org/10.1016/j.cose.2023.103504.
|
[12] |
SHAO Shuo, YANG Wenyuan, GU Hanlin, et al. FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model[J]. IEEE Transactions on Dependable and Secure Computing, 2024 (99): 1-18.
|
[13] |
TEKGUL B G A, XIA Yuxi, MARCHAL S, et al. WAFFLE: Watermarking in Federated Learning[C]// IEEE. 2021 40th International Symposium on Reliable Distributed Systems (SRDS). New York: IEEE, 2021: 310-320.
|
[14] |
YU Shuyang, HONG Junyuan, ZENG Yi, et al.Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning[EB/OL]. (2023-12-06)[2024-05-23]. https://arxiv.org/abs/2312.03205v1.
|
[15] |
LI Bowen, FAN Lixin, GU Hanlin, et al. FedIPR: Ownership Verification for Federated Deep Neural Network Models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(4): 4521-4536.
|
[16] |
NIE Hewang, LU Songfeng. FedCRMW: Federated Model Ownership Verification with Compression-Resistant Model Watermarking[EB/OL]. (2024-03-26)[2024-05-23]. https://www.sciencedirect.com/science/article/abs/pii/S0957417424006420?via%3Dihub.
|
[17] |
NIE Hewang, LU Songfeng. PersistVerify: Federated Model Ownership Verification with Spatial Attention and Boundary Sampling[EB/OL]. (2024-03-21)[2024-05-23]. https://www.sciencedirect.com/science/article/abs/pii/S0950705124003101?via%3Dihub.
|
[18] |
YANG Wenyuan, SHAO Shuo, YANG Yue, et al. Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring[J]. ACM Transactions on Intelligent Systems and Technology, 2024, 15(1): 1-25.
|
[19] |
YANG Wenyuan, YIN Yuguo, ZHU Gongxi, et al. FedZKP: Federated Model Ownership Verification with Zero-Knowledge Proof[EB/OL]. (2023-05-10)[2024-05-23]. https://arxiv.org/abs/2305.04507v2.
|
[20] |
UCHIDA Y, NAGAI Y, SAKAZAWA S, et al. Embedding Watermarks into Deep Neural Networks[C]// ACM. Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. New York: ACM, 2017: 269-277.
|
[21] |
LI Fangqi, WANG Shilin, LIEW A W C. Watermarking Protocol for Deep Neural Network Ownership Regulation in Federated Learning[C]// IEEE. 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). New York: IEEE, 2022: 1-4.
|
[22] |
YANG Wenyuan, ZHU Gongxi, YIN Yuguo, et al. FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature[EB/OL]. (2023-05-10)[2024-05-23]. https://arxiv.org/abs/2305.06085v1.
|
[23] |
LIANG Junchuan, WANG Rong. FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking[EB/OL]. (2023-06-02)[2024-05-23]. https://arxiv.org/abs/2306.01356v1.
|
[24] |
XU Yang, TAN Yunlin, ZHANG Cheng, et al. RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection[EB/OL]. (2024-02-29)[2024-05-23]. https://arxiv.org/abs/2402.19054v1.
|