Algorithmic fairness in artificial intelligence for medicine and healthcare

RJ Chen, JJ Wang, DFK Williamson, TY Chen… - Nature biomedical …, 2023 - nature.com
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

[HTML][HTML] Federated learning for 6G-enabled secure communication systems: a comprehensive survey

D Sirohi, N Kumar, PS Rana, S Tanwar, R Iqbal… - Artificial Intelligence …, 2023 - Springer
Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …

Fedgan-ids: Privacy-preserving ids using gan and federated learning

A Tabassum, A Erbad, W Lebda, A Mohamed… - Computer …, 2022 - Elsevier
Federated Learning (FL) is a promising distributed training model that aims to minimize the
data sharing to enhance privacy and performance. FL requires sufficient and diverse training …

Dafkd: Domain-aware federated knowledge distillation

H Wang, Y Li, W Xu, R Li, Y Zhan… - Proceedings of the …, 2023 - openaccess.thecvf.com
Federated Distillation (FD) has recently attracted increasing attention for its efficiency in
aggregating multiple diverse local models trained from statistically heterogeneous data of …

PerFED-GAN: Personalized federated learning via generative adversarial networks

X Cao, G Sun, H Yu, M Guizani - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Federated learning is gaining popularity as a distributed machine learning method that can
be used to deploy AI-dependent Internet of Things applications while protecting client data …

Cgan-based collaborative intrusion detection for uav networks: A blockchain-empowered distributed federated learning approach

X He, Q Chen, L Tang, W Wang… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Numerous resource-constrained Internet of Things (IoT) devices make the edge IoT
consisting of unmanned aerial vehicles (UAVs) vulnerable to network intrusion. Therefore, it …

Comprehensive analysis of privacy leakage in vertical federated learning during prediction

X Jiang, X Zhou, J Grossklags - Proceedings on Privacy …, 2022 - petsymposium.org
Vertical federated learning (VFL), a variant of federated learning, has recently attracted
increasing attention. An active party having the true labels jointly trains a model with other …

Federated generative model on multi-source heterogeneous data in iot

Z Xiong, W Li, Z Cai - Proceedings of the AAAI Conference on Artificial …, 2023 - ojs.aaai.org
The study of generative models is a promising branch of deep learning techniques, which
has been successfully applied to different scenarios, such as Artificial Intelligence and the …

Algorithm fairness in ai for medicine and healthcare

RJ Chen, TY Chen, J Lipkova, JJ Wang… - arXiv preprint arXiv …, 2021 - arxiv.org
In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

Towards data-independent knowledge transfer in model-heterogeneous federated learning

J Zhang, S Guo, J Guo, D Zeng, J Zhou… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Federated Distillation (FD) extends classic Federated Learning (FL) to a more general
training framework that enables model-heterogeneous collaborative learning by Knowledge …