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PPRD-FL: Privacy-Preserving Federated Learning Based on Randomized Parameter Selection and Dynamic Local Differential Privacy
As traditional federated learning algorithms often fall short in providing privacy protection, a growing body of research integrates local differential privacy methods into federated learning to strengthen privacy guarantees. However,...
文献获取Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS
Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPSdoi:10.1016/j.sysarc.2024.103108...
FedMPS: A Robust Differential Privacy Federated Learning Based on Local Model Partition and Sparsification for Heterogeneous IIoT Data
In the emerging Industrial Internet of Things (IIoT) applications, federated learning (FL) enables model training without the need to transmit raw data ...
Federated Learning With Sparsified Model Perturbation: Improving Accuracy Under Client-Level Differential Privacy
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keeping their training data locally has received great a...
Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework
Federated Learning (FL) is a distributed machine learning framework that inherently allows edge devices to maintain their local training data, thus prov...
Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism
Federated learning enables the development of robust models without accessing users data directly. However, recent studies indicate that federated learn...
DP-Poison: Poisoning Federated Learning under the Cover of Differential Privacy
Federated learning (FL) enables resource-constrained node devices to learn a shared model while keeping the training data local. Since recent research h...
Federated transfer learning with differential privacy for multi-omics survival analysis
Multi-omics data often suffer from the "big p , small n " problem where the dimensionality of features is significantly larger than the sample size, ma...
FEDERATED LEARNING SYSTEM AND METHOD WITH ADAPTIVE NOISE AND DIFFERENTIAL PRIVACY
A wireless communication network method and system that includes a central server, cellular base stations, edge computing devices, and client devices. T...
Evaluating the Impact of Mobility on Differentially Private Federated Learning
This paper investigates differential privacy in federated learning. This topic has been actively examined in conventional network environments, but few ...
