Membership Inference Attacks and Defenses in Federated Learning: A Survey
Federated learning is a decentralized machine learning approach where clients train
models locally and share model updates to develop a global model. This enables low …
models locally and share model updates to develop a global model. This enables low …
PPFed: A Privacy-Preserving and Personalized Federated Learning Framework
Federated learning is a distributed learning paradigm where a global model is trained using
data samples from multiple clients but without the necessity of sharing raw data samples …
data samples from multiple clients but without the necessity of sharing raw data samples …
Staged noise perturbation for privacy-preserving federated learning
Federated learning (FL) is a distributed machine learning paradigm that addresses the
challenges of privacy leakage and data silos by collaboratively training the global model …
challenges of privacy leakage and data silos by collaboratively training the global model …
MIGAN: A Privacy Leakage Evaluation Scheme for CIoT-Based Federated Learning Users
Federated Learning (FL) in Consumer Internet of Things (CIoT) encounters significant
privacy security threats when collaborative training Machine Learning models using data …
privacy security threats when collaborative training Machine Learning models using data …
SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning
J Xia, M Wu, P Li - Future Generation Computer Systems, 2025 - Elsevier
Traffic classification is essential for network management and optimization, enhancing user
experience, network performance, and security. However, evolving technologies and …
experience, network performance, and security. However, evolving technologies and …
Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A Survey
With the rapid development of artificial intelligence and a new generation of network
technologies, the Internet of Things (IoT) is expanding worldwide. Malicious agents …
technologies, the Internet of Things (IoT) is expanding worldwide. Malicious agents …
PT-ADP: A personalized privacy-preserving federated learning scheme based on transaction mechanism
Differential privacy (DP) is a widely used technique for enhancing privacy in federated
learning (FL) frameworks, whereby noise is added to the datasets or learning parameters to …
learning (FL) frameworks, whereby noise is added to the datasets or learning parameters to …
A privacy-preserving federated learning protocol with a secure data aggregation for the Internet of Everything
S Basudan - Computer Communications, 2024 - Elsevier
Although there are significant advantages to the popular use of connected devices on the
Internet of Everything, there remain distinct concerns surrounding privacy. Federated …
Internet of Everything, there remain distinct concerns surrounding privacy. Federated …
[HTML][HTML] Leveraging Multiple Adversarial Perturbation Distances for Enhanced Membership Inference Attack in Federated Learning
F Xia, Y Liu, B Jin, Z Yu, X Cai, H Li, Z Zha, D Hou… - Symmetry, 2024 - mdpi.com
In recent years, federated learning (FL) has gained significant attention for its ability to
protect data privacy during distributed training. However, it also introduces new privacy …
protect data privacy during distributed training. However, it also introduces new privacy …
Towards a more secure reconstruction-based anomaly detection model for power transformer differential protection
Introduction Cyberattacks against Power Transformer Differential Protection (PTDP) have
the potential to cause significant disruption and widespread blackouts in power …
the potential to cause significant disruption and widespread blackouts in power …