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Federated Learning

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A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.

PloS one
Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However,...

Federated learning based reference evapotranspiration estimation for distributed crop fields.

PloS one
Water resource management and sustainable agriculture rely heavily on accurate Reference Evapotranspiration (ETo). Efforts have been made to simplify the (ETo) estimation using machine learning models. The existing approaches are limited to a single ...

Quantum federated learning with pole-angle quantum local training and trainable measurement.

Neural networks : the official journal of the International Neural Network Society
Recently, quantum federated learning (QFL) has received significant attention as an innovative paradigm. QFL has remarkable features by employing quantum neural networks (QNNs) instead of conventional neural networks owing to quantum supremacy. In or...

Replica tree-based federated learning using limited data.

Neural networks : the official journal of the International Neural Network Society
Learning from limited data has been extensively studied in machine learning, considering that deep neural networks achieve optimal performance when trained using a large amount of samples. Although various strategies have been proposed for centralize...

DKCN-Net: Deep kronecker convolutional neural network-based lung disease detection with federated learning.

Computational biology and chemistry
In the healthcare field, lung disease detection techniques based on deep learning (DL) are widely used. However, achieving high stability while maintaining privacy remains a challenge. To address this, this research employs Federated Learning (FL), e...

From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare.

Medical image analysis
Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centers while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federate...

Vertical federated learning based on data subset representation for healthcare application.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Artificial intelligence is increasingly essential for disease classification and clinical diagnosis tasks in healthcare. Given the strict privacy needs of healthcare data, Vertical Federated Learning (VFL) has been introduce...

Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks.

Sensors (Basel, Switzerland)
The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning fr...

Multi-modal dataset creation for federated learning with DICOM-structured reports.

International journal of computer assisted radiology and surgery
Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging...

A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.

Scientific reports
The traditional molecular-based identification (TMID) technique of new infections from genome sequences (GSs) has made significant contributions so far. However, due to the sensitive nature of the medical data, the TMID technique of transferring the ...