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 …

Survey: Image mixing and deleting for data augmentation

H Naveed, S Anwar, M Hayat, K Javed… - Engineering applications of …, 2024 - Elsevier
Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting
and enhance their generalization and performance, various methods have been suggested …

Privacy-preserving face recognition using trainable feature subtraction

Y Mi, Z Zhong, Y Huang, J Ji, J Xu… - Proceedings of the …, 2024 - openaccess.thecvf.com
The widespread adoption of face recognition has led to increasing privacy concerns as
unauthorized access to face images can expose sensitive personal information. This paper …

Distributed contrastive learning for medical image segmentation

Y Wu, D Zeng, Z Wang, Y Shi, J Hu - Medical Image Analysis, 2022 - Elsevier
Supervised deep learning needs a large amount of labeled data to achieve high
performance. However, in medical imaging analysis, each site may only have a limited …

Quantum federated learning through blind quantum computing

W Li, S Lu, DL Deng - Science China Physics, Mechanics & Astronomy, 2021 - Springer
Private distributed learning studies the problem of how multiple distributed entities
collaboratively train a shared deep network with their private data unrevealed. With the …

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

R Zhang, S Guo, J Wang, X **e, 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 …