Privacy-preserving federated edge learning: Modeling and optimization
In this letter, we consider the personalized differential privacy (DP) based federated edge
learning system. Each edge device adds DP noise to its local machine learning (ML) model …
learning system. Each edge device adds DP noise to its local machine learning (ML) model …
Efficient privacy-preserving ML for IoT: Cluster-based split federated learning scheme for non-IID data
M Arafeh, M Wazzeh, H Ould-Slimane… - 2023 7th Cyber …, 2023 - ieeexplore.ieee.org
This paper proposes a scheme addressing the challenges of integrating privacy-preserving
distributed machine learning in the Internet of Things (IoT) context while improving the …
distributed machine learning in the Internet of Things (IoT) context while improving the …
DynamicNet: Efficient Federated Learning for Mobile Edge Computing With Dynamic Privacy Budget and Aggregation Weights
Z Li, M Duan, S Yu, W Yang - IEEE Transactions on Consumer …, 2024 - ieeexplore.ieee.org
Mobile Edge Computing (MEC) has been successfully applied to smart homes, mobile
devices, and other electronic products, offering reduced communication latency and …
devices, and other electronic products, offering reduced communication latency and …
Optimizing the numbers of queries and replies in convex federated learning with differential privacy
Federated learning (FL) empowers distributed clients to collaboratively train a shared
machine learning model through exchanging parameter information. Despite the fact that FL …
machine learning model through exchanging parameter information. Despite the fact that FL …
One-shot federated learning without server-side training
Federated Learning (FL) has recently made significant progress as a new machine learning
paradigm for privacy protection. Due to the high communication cost of traditional FL, one …
paradigm for privacy protection. Due to the high communication cost of traditional FL, one …
A novel local differential privacy federated learning under multi-privacy regimes
Local differential privacy federated learning (LDP-FL) is a framework to achieve high local
data privacy protection while training the model in a decentralized environment. Currently …
data privacy protection while training the model in a decentralized environment. Currently …
Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model …
The proliferation of IoT devices has led to an unprecedented integration of machine learning
techniques, raising concerns about data privacy. To address these concerns, federated …
techniques, raising concerns about data privacy. To address these concerns, federated …
Social-aware clustered federated learning with customized privacy preservation
A key feature of federated learning (FL) is to preserve the data privacy of end users.
However, there still exist potential privacy leakage in exchanging gradients under FL. As a …
However, there still exist potential privacy leakage in exchanging gradients under FL. As a …
Contribution‐based Federated Learning client selection
W Lin, Y Xu, B Liu, D Li, T Huang… - International Journal of …, 2022 - Wiley Online Library
Federated Learning (FL), as a privacy‐preserving machine learning paradigm, has been
thrusted into the limelight. As a result of the physical bandwidth constraint, only a small …
thrusted into the limelight. As a result of the physical bandwidth constraint, only a small …
[PDF][PDF] Performance analysis and optimization in privacy-preserving federated learning
As a means of decentralized machine learning, federated learning (FL) has recently drawn
considerable attentions. One of the prominent advantages of FL is its capability of preventing …
considerable attentions. One of the prominent advantages of FL is its capability of preventing …