no code implementations • 29 Jan 2024 • Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, Xiaofeng Meng
Federated learning enhanced by differential privacy has emerged as a popular approach to better safeguard the privacy of client-side data by protecting clients' contributions during the training process.
no code implementations • ICCV 2023 • Junxu Liu, Mingsheng Xue, Jian Lou, XiaoYu Zhang, Li Xiong, Zhan Qin
However, existing methods focus exclusively on unlearning from standard training models and do not apply to adversarial training models (ATMs) despite their popularity as effective defenses against adversarial examples.
2 code implementations • VLDB 2022 2021 • Junxu Liu, Li Xiong, Jinfei Liu, Xiaofeng Meng
The challenge is how to use such information without biasing the joint model.