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 …
Federated Learning with Privacy-preserving and Model IP-right-protection
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 …
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
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 …
requires innovation in privacy-preserving machine learning so models can train on sensitive …
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 …
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 …
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 …
edge intelligent collaborative computing, and its security and privacy issues have attracted …
Reconstructing training data from model gradient, provably
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 …
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
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 …
federated learning algorithms and frameworks have been developed to ensure model …
A secure and efficient federated learning framework for nlp
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 …
(FL) frameworks. Existing solutions either involve a trusted aggregator or require …
Data reconstruction attacks and defenses: A systematic evaluation
Reconstruction attacks and defenses are essential in understanding the data leakage
problem in machine learning. However, prior work has centered around empirical …
problem in machine learning. However, prior work has centered around empirical …