Fedsens: A federated learning approach for smart health sensing with class imbalance in resource constrained edge computing
The advance of mobile sensing and edge computing has brought new opportunities for
abnormal health detection (AHD) systems where edge devices such as smartphones and …
abnormal health detection (AHD) systems where edge devices such as smartphones and …
Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring
In-home health monitoring has attracted great attention for the ageing population worldwide.
With the abundant user health data accessed by Internet of Things (IoT) devices and recent …
With the abundant user health data accessed by Internet of Things (IoT) devices and recent …
A framework for edge-assisted healthcare data analytics using federated learning
With the emergence of wearable technology, IoT, and Edge computing, the nature of
healthcare is rapidly shifting towards digital health aided by these ICT technologies. At the …
healthcare is rapidly shifting towards digital health aided by these ICT technologies. At the …
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 …
Federated deep learning for heterogeneous edge computing
Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as
smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …
smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …
Lotteryfl: Empower edge intelligence with personalized and communication-efficient federated learning
With the proliferation of mobile computing and Internet of Things (IoT), massive mobile and
IoT devices are connected to the Internet. These devices are generating a huge amount of …
IoT devices are connected to the Internet. These devices are generating a huge amount of …
Fed xData: A federated learning framework for enabling contextual health monitoring in a cloud-edge network
Due to the rapid recent development of cloud-edge networks, smart devices can facilitate
rapid access to patients' health information. Success has been achieved in the healthcare …
rapid access to patients' health information. Success has been achieved in the healthcare …
Privacy-aware and resource-saving collaborative learning for healthcare in cloud computing
Electronic health records (EHR), generated in healthcare, contain extensive digital
information, such as diagnoses, medications and complications. Recently, many studies …
information, such as diagnoses, medications and complications. Recently, many studies …
Federated contrastive learning for dermatological disease diagnosis via on-device learning
Deep learning models have been deployed in an increasing number of edge and mobile
devices to provide healthcare. These models rely on training with a tremendous amount of …
devices to provide healthcare. These models rely on training with a tremendous amount of …
Federated learning with non-iid data
Federated learning enables resource-constrained edge compute devices, such as mobile
phones and IoT devices, to learn a shared model for prediction, while keeping the training …
phones and IoT devices, to learn a shared model for prediction, while keeping the training …
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