Incentivizing differentially private federated learning: A multidimensional contract approach
Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a
machine learning model in a decentralized manner. Specifically, the data owners (eg, IoT …
machine learning model in a decentralized manner. Specifically, the data owners (eg, IoT …
Adaptive privacy preserving deep learning algorithms for medical data
Deep learning holds a great promise of revolutionizing healthcare and medicine.
Unfortunately, various inference attack models demonstrated that deep learning puts …
Unfortunately, various inference attack models demonstrated that deep learning puts …
Differentially private and communication efficient collaborative learning
Collaborative learning has received huge interests due to its capability of exploiting the
collective computing power of the wireless edge devices. However, during the learning …
collective computing power of the wireless edge devices. However, during the learning …
From noisy fixed-point iterations to private ADMM for centralized and federated learning
We study differentially private (DP) machine learning algorithms as instances of noisy fixed-
point iterations, in order to derive privacy and utility results from this well-studied framework …
point iterations, in order to derive privacy and utility results from this well-studied framework …
Boosting accuracy of differentially private federated learning in industrial IoT with sparse responses
Empowered by 5G, it has been extensively explored by existing works on the deployment of
differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT) …
differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT) …
Preserving Privacy in Fine-grained Data Distillation with Sparse Answers for Efficient Edge Computing
K Pan, M Gong, K Feng, H Li - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
In the field of internet of things (IoT), data distillation has been thought of as a key method to
condense the original real dataset into a tiny synthetic dataset with less training burden …
condense the original real dataset into a tiny synthetic dataset with less training burden …
An Optimized Sparse Response Mechanism for Differentially Private Federated Learning
Federated Learning (FL) enables geo-distributed clients to collaboratively train a learning
model without exposing their private data. By only exposing local model parameters, FL well …
model without exposing their private data. By only exposing local model parameters, FL well …
Federated learning of oligonucleotide drug molecule thermodynamics with differentially private admm-based svm
A crucial step to assure drug safety is predicting off-target binding. For oligonucleotide drugs
this requires learning the relevant thermodynamics from often large-scale data distributed …
this requires learning the relevant thermodynamics from often large-scale data distributed …
Balancing Privacy and Accuracy using Significant Gradient Protection in Federated Learning
Previous state-of-the-art studies have demonstrated that adversaries can access sensitive
user data by membership inference attacks (MIAs) in Federated Learning (FL). Introducing …
user data by membership inference attacks (MIAs) in Federated Learning (FL). Introducing …
Distributed and federated learning of support vector machines and applications
S Tavara - 2022 - diva-portal.org
Machine Learning (ML) has achieved remarkable success in solving classification,
regression, and related problems over the past decade. In particular the exponential growth …
regression, and related problems over the past decade. In particular the exponential growth …