Efficiency optimization techniques in privacy-preserving federated learning with homomorphic encryption: A brief survey
Federated learning (FL) offers distributed machine learning on edge devices. However, the
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …
An overview of implementing security and privacy in federated learning
K Hu, S Gong, Q Zhang, C Seng, M **a… - Artificial Intelligence …, 2024 - Springer
Federated learning has received a great deal of research attention recently, with privacy
protection becoming a key factor in the development of artificial intelligence. Federated …
protection becoming a key factor in the development of artificial intelligence. Federated …
Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …
preservation demands in artificial intelligence. As machine learning, federated learning is …
PPFL: Privacy-preserving federated learning with trusted execution environments
We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …
A survey of trustworthy federated learning: Issues, solutions, and challenges
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
FedML-HE: An efficient homomorphic-encryption-based privacy-preserving federated learning system
Federated Learning trains machine learning models on distributed devices by aggregating
local model updates instead of local data. However, privacy concerns arise as the …
local model updates instead of local data. However, privacy concerns arise as the …
Model architecture level privacy leakage in neural networks
Privacy leakage is one of the most critical issues in machine learning and has attracted
growing interest for tasks such as demonstrating potential threats in model attacks and …
growing interest for tasks such as demonstrating potential threats in model attacks and …
One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis
Curation of large, diverse MRI datasets via multi-institutional collaborations can help
improve learning of generalizable synthesis models that reliably translate source-onto target …
improve learning of generalizable synthesis models that reliably translate source-onto target …
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 …
A survey of trustworthy federated learning with perspectives on security, robustness and privacy
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly
benefited human society. Among various AI technologies, Federated Learning (FL) stands …
benefited human society. Among various AI technologies, Federated Learning (FL) stands …