Adversarial attacks and defenses in deep learning: From a perspective of cybersecurity
The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …
applications in a broad set of domains. However, the potential risks caused by adversarial …
Sok: Model inversion attack landscape: Taxonomy, challenges, and future roadmap
SV Dibbo - 2023 IEEE 36th Computer Security Foundations …, 2023 - ieeexplore.ieee.org
A crucial module of the widely applied machine learning (ML) model is the model training
phase, which involves large-scale training data, often including sensitive private data. ML …
phase, which involves large-scale training data, often including sensitive private data. ML …
Exploiting defenses against gan-based feature inference attacks in federated learning
X Luo, X Zhang - ar** the training data
decentralized and private. However, in IoT systems, inherent heterogeneity in processing …
decentralized and private. However, in IoT systems, inherent heterogeneity in processing …
Ringsfl: An adaptive split federated learning towards taming client heterogeneity
Federated learning (FL) has gained increasing attention due to its ability to collaboratively
train while protecting client data privacy. However, vanilla FL cannot adapt to client …
train while protecting client data privacy. However, vanilla FL cannot adapt to client …