A systematic review of federated learning: Challenges, aggregation methods, and development tools
BS Guendouzi, S Ouchani, HEL Assaad… - Journal of Network and …, 2023 - Elsevier
Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …
ized machine learning approach, facilitating collaborative model training across numerous …
Federated learning for medical image analysis with deep neural networks
S Nazir, M Kaleem - Diagnostics, 2023 - mdpi.com
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-
art performance in image classification and segmentation tasks, aiding disease diagnosis …
art performance in image classification and segmentation tasks, aiding disease diagnosis …
Reconciling privacy and accuracy in AI for medical imaging
A Ziller, TT Mueller, S Stieger, LF Feiner… - Nature Machine …, 2024 - nature.com
Artificial intelligence (AI) models are vulnerable to information leakage of their training data,
which can be highly sensitive, for example, in medical imaging. Privacy-enhancing …
which can be highly sensitive, for example, in medical imaging. Privacy-enhancing …
Nvidia flare: Federated learning from simulation to real-world
Federated learning (FL) enables building robust and generalizable AI models by leveraging
diverse datasets from multiple collaborators without centralizing the data. We created …
diverse datasets from multiple collaborators without centralizing the data. We created …
Federated learning for medical image analysis: A survey
Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …
the small sample size problem. Many recent studies suggest using multi-domain data …
Blockchain-based personalized federated learning for internet of medical things
The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing
services have enabled the Internet of Medical Things (IoMT) to provide various healthcare …
services have enabled the Internet of Medical Things (IoMT) to provide various healthcare …
Privacy preservation for federated learning in health care
Artificial intelligence (AI) shows potential to improve health care by leveraging data to build
models that can inform clinical workflows. However, access to large quantities of diverse …
models that can inform clinical workflows. However, access to large quantities of diverse …
[HTML][HTML] Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions
Abstract Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a
reputation for not only building Machine Learning (ML) models that rely on distributed …
reputation for not only building Machine Learning (ML) models that rely on distributed …
Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging
S Tayebi Arasteh, A Ziller, C Kuhl, M Makowski… - Communications …, 2024 - nature.com
Background Artificial intelligence (AI) models are increasingly used in the medical domain.
However, as medical data is highly sensitive, special precautions to ensure its protection are …
However, as medical data is highly sensitive, special precautions to ensure its protection are …
Two-level privacy-preserving framework: Federated learning for attack detection in the consumer internet of things
E Rabieinejad, A Yazdinejad… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
As the adoption of Consumer Internet of Things (CIoT) devices surges, so do concerns about
security vulnerabilities and privacy breaches. Given their integration into daily life and data …
security vulnerabilities and privacy breaches. Given their integration into daily life and data …