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Privacy and fairness in federated learning: On the perspective of tradeoff
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 …
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
Survey: Image mixing and deleting for data augmentation
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 …
and enhance their generalization and performance, various methods have been suggested …
Large language models can be strong differentially private learners
X Li, F Tramer, P Liang, T Hashimoto - ar**
each user's data private. Recently, a growing body of work has demonstrated that an …
each user's data private. Recently, a growing body of work has demonstrated that an …
Privacy-preserving face recognition using trainable feature subtraction
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 …
unauthorized access to face images can expose sensitive personal information. This paper …
Distributed contrastive learning for medical image segmentation
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 …
performance. However, in medical imaging analysis, each site may only have a limited …
Quantum federated learning through blind quantum computing
Private distributed learning studies the problem of how multiple distributed entities
collaboratively train a shared deep network with their private data unrevealed. With the …
collaboratively train a shared deep network with their private data unrevealed. With the …
A survey on gradient inversion: Attacks, defenses and future directions
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 …
which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of …