A privacy-preserving social computing framework for health management using federated learning
Currently, health management driven by intelligent means is a general demand of social
systems. Although a number of researchers have paid attention to such areas, they have …
systems. Although a number of researchers have paid attention to such areas, they have …
Hierarchical federated learning with social context clustering-based participant selection for internet of medical things applications
The proliferation in embedded and communication technologies made the concept of the
Internet of Medical Things (IoMT) a reality. Individuals' physical and physiological status can …
Internet of Medical Things (IoMT) a reality. Individuals' physical and physiological status can …
A privacy-preserving distributed contextual federated online learning framework with big data support in social recommender systems
Nowadays, the booming demand of big data analytics and the constraints of computational
ability and network bandwidth have made it difficult for a stand-alone agent/service provider …
ability and network bandwidth have made it difficult for a stand-alone agent/service provider …
[HTML][HTML] A review of privacy enhancement methods for federated learning in healthcare systems
Federated learning (FL) provides a distributed machine learning system that enables
participants to train using local data to create a shared model by eliminating the requirement …
participants to train using local data to create a shared model by eliminating the requirement …
Edge intelligence: Federated learning-based privacy protection framework for smart healthcare systems
Federated learning methods offer secured monitor services and privacy-preserving
paradigms to end-users and organisations in the Internet of Things networks such as smart …
paradigms to end-users and organisations in the Internet of Things networks such as smart …
Fedcare: Federated learning for resource-constrained healthcare devices in iomt system
In social IoMT systems, resource-constrained devices face the challenges of limited
computation, bandwidth, and privacy in the deployment of deep learning models. Federated …
computation, bandwidth, and privacy in the deployment of deep learning models. Federated …
FEEL: A federated edge learning system for efficient and privacy-preserving mobile healthcare
With the prosperity of artificial intelligence, neural networks have been increasingly applied
in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile …
in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile …
Privacy is what we care about: Experimental investigation of federated learning on edge devices
A Das, T Brunschwiler - Proceedings of the First International Workshop …, 2019 - dl.acm.org
Federated Learning enables training of a general model through edge devices without
sending raw data to the cloud. Hence, this approach is attractive for digital health …
sending raw data to the cloud. Hence, this approach is attractive for digital health …
Preserving user privacy for machine learning: Local differential privacy or federated machine learning?
The growing number of mobile and IoT devices has nourished many intelligent applications.
In order to produce high-quality machine learning models, they constantly access and …
In order to produce high-quality machine learning models, they constantly access and …
Privacy-preserving federated deep learning with irregular users
Federated deep learning has been widely used in various fields. To protect data privacy,
many privacy-preservingapproaches have been designed and implemented in various …
many privacy-preservingapproaches have been designed and implemented in various …