Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
Adoption of federated learning for healthcare informatics: Emerging applications and future directions
The smart healthcare system has improved the patients quality of life (QoL), where the
records are being analyzed remotely by distributed stakeholders. It requires a voluminous …
records are being analyzed remotely by distributed stakeholders. It requires a voluminous …
Federated learning and blockchain-enabled fog-IoT platform for wearables in predictive healthcare
Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in
several healthcare services are increased. Among the services that benefit from the usage of …
several healthcare services are increased. Among the services that benefit from the usage of …
Distributed anomaly detection in smart grids: a federated learning-based approach
The smart grid integrates Information and Communication Technologies (ICT) into the
traditional power grid to manage the generation, distribution, and consumption of electrical …
traditional power grid to manage the generation, distribution, and consumption of electrical …
Decentralized federated learning: A survey and perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data
Training on multiple diverse data sources is critical to ensure unbiased and generalizable
AI. In healthcare, data privacy laws prohibit data from being moved outside the country of …
AI. In healthcare, data privacy laws prohibit data from being moved outside the country of …
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 …
Security of blockchain and AI-empowered smart healthcare: application-based analysis
A Alabdulatif, I Khalil, M Saidur Rahman - Applied Sciences, 2022 - mdpi.com
A smart device carries a great amount of sensitive patient data as it offers innovative and
enhanced functionalities in the smart healthcare system. Moreover, the components of …
enhanced functionalities in the smart healthcare system. Moreover, the components of …
Gradient boosting for health IoT federated learning
Federated learning preserves the privacy of user data through Machine Learning (ML). It
enables the training of an ML model during this process. The Healthcare Internet of Things …
enables the training of an ML model during this process. The Healthcare Internet of Things …
Federated learning for healthcare applications
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 …
become critical for healthcare tasks like in medical image analysis and human behavior …