Blockchain meets federated learning in healthcare: A systematic review with challenges and opportunities

R Myrzashova, SH Alsamhi… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Recently, innovations in the Internet of Medical Things (IoMT), information and
communication technologies, and machine learning (ML) have enabled smart healthcare …

Intelligent healthcare: Integration of emerging technologies and Internet of Things for humanity

VA Dang, Q Vu Khanh, VH Nguyen, T Nguyen… - Sensors, 2023 - mdpi.com
Health is gold, and good health is a matter of survival for humanity. The development of the
healthcare industry aligns with the development of humans throughout history. Nowadays …

Review on security of federated learning and its application in healthcare

H Li, C Li, J Wang, A Yang, Z Ma, Z Zhang… - Future Generation …, 2023 - Elsevier
Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …

[HTML][HTML] Internet of robotic things for mobile robots: concepts, technologies, challenges, applications, and future directions

H Kabir, ML Tham, YC Chang - Digital Communications and Networks, 2023 - Elsevier
Abstract Nowadays, Multi Robotic System (MRS) consisting of different robot shapes, sizes
and capabilities has received significant attention from researchers and are being deployed …

Handling privacy-sensitive medical data with federated learning: challenges and future directions

O Aouedi, A Sacco, K Piamrat… - IEEE journal of …, 2022 - ieeexplore.ieee.org
Recent medical applications are largely dominated by the application of Machine Learning
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …

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 …

Federated learning for healthcare applications

A Chaddad, Y Wu, C Desrosiers - IEEE internet of things …, 2023 - ieeexplore.ieee.org
Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

Integrated CNN and federated learning for COVID-19 detection on chest X-ray images

Z Li, X Xu, X Cao, W Liu, Y Zhang… - … /ACM Transactions on …, 2022 - ieeexplore.ieee.org
Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and
safety and deep learning (DL) is expected to be the most powerful method for efficient …

Dres-fl: Dropout-resilient secure federated learning for non-iid clients via secret data sharing

J Shao, Y Sun, S Li, J Zhang - Advances in Neural …, 2022 - proceedings.neurips.cc
Federated learning (FL) strives to enable collaborative training of machine learning models
without centrally collecting clients' private data. Different from centralized training, the local …

Data-driven analytics leveraging artificial intelligence in the era of COVID-19: an insightful review of recent developments

A Majeed, SO Hwang - Symmetry, 2021 - mdpi.com
This paper presents the role of artificial intelligence (AI) and other latest technologies that
were employed to fight the recent pandemic (ie, novel coronavirus disease-2019 (COVID …