Privacy and fairness in Federated learning: on the perspective of Tradeoff

H Chen, T Zhu, T Zhang, W Zhou, PS Yu - ACM Computing Surveys, 2023 - dl.acm.org
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

Federated Learning with Privacy-preserving and Model IP-right-protection

Q Yang, A Huang, L Fan, CS Chan, JH Lim… - Machine Intelligence …, 2023 - Springer
In the past decades, artificial intelligence (AI) has achieved unprecedented success, where
statistical models become the central entity in AI. However, the centralized training and …

End-to-end privacy preserving deep learning on multi-institutional medical imaging

G Kaissis, A Ziller, J Passerat-Palmbach… - Nature Machine …, 2021 - nature.com
Using large, multi-national datasets for high-performance medical imaging AI systems
requires innovation in privacy-preserving machine learning so models can train on sensitive …

A survey on gradient inversion: Attacks, defenses and future directions

R Zhang, S Guo, J Wang, X Xie, D Tao - arXiv preprint arXiv:2206.07284, 2022 - arxiv.org
Recent studies have shown that the training samples can be recovered from gradients,
which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of …

Precode-a generic model extension to prevent deep gradient leakage

D Scheliga, P Mäder… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Collaborative training of neural networks leverages distributed data by exchanging gradient
information between different clients. Although training data entirely resides with the clients …

Security of federated learning for cloud‐edge intelligence collaborative computing

J Yang, J Zheng, Z Zhang, QI Chen… - … Journal of Intelligent …, 2022 - Wiley Online Library
Federated Learning (FL) is one of the key technologies to solve privacy protection for cloud‐
edge intelligent collaborative computing, and its security and privacy issues have attracted …

Reconstructing training data from model gradient, provably

Z Wang, J Lee, Q Lei - International Conference on Artificial …, 2023 - proceedings.mlr.press
Understanding when and how much a model gradient leaks information about the training
sample is an important question in privacy. In this paper, we present a surprising result …

[HTML][HTML] Gradient-based defense methods for data leakage in vertical federated learning

W Chang, T Zhu - Computers & Security, 2024 - Elsevier
Research on federated learning has continued to develop over the past few years. Many
federated learning algorithms and frameworks have been developed to ensure model …

A secure and efficient federated learning framework for nlp

J Deng, C Wang, X Meng, Y Wang, J Li, S Lin… - arXiv preprint arXiv …, 2022 - arxiv.org
In this work, we consider the problem of designing secure and efficient federated learning
(FL) frameworks. Existing solutions either involve a trusted aggregator or require …

Data reconstruction attacks and defenses: A systematic evaluation

S Liu, Z Wang, Y Chen, Q Lei - arXiv preprint arXiv:2402.09478, 2024 - arxiv.org
Reconstruction attacks and defenses are essential in understanding the data leakage
problem in machine learning. However, prior work has centered around empirical …