AIMC Topic: Federated Learning

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

PloS one Feb 11, 2025
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,...
Magnetic Resonance Imaging Humans Privacy Federated Learning Brain Neoplasms Deep Learning Glioma
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DKCN-Net: Deep kronecker convolutional neural network-based lung disease detection with federated learning.

Computational biology and chemistry Feb 8, 2025
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...
Federated Learning Convolutional Neural Networks Lung Diseases Humans Tomography, X-Ray Computed Deep Learning Neural Networks, Computer
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Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging.

IEEE transactions on neural networks and learning systems Feb 6, 2025
In clinical practice, computed tomography (CT) is an important noninvasive inspection technology to provide patients' anatomical information. However, its potential radiation risk is an unavoidable problem that raises people's concerns. Recently, dee...
Neural Networks, Computer Algorithms Humans Image Processing, Computer-Assisted Physics Federated Learning Tomography, X-Ray Computed Deep Learning
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Federated learning based reference evapotranspiration estimation for distributed crop fields.

PloS one Feb 5, 2025
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 ...
Pakistan Crops, Agricultural Agriculture Weather Plant Transpiration Machine Learning Federated Learning
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Multi-modal dataset creation for federated learning with DICOM-structured reports.

International journal of computer assisted radiology and surgery Feb 3, 2025
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...
Federated Learning Tomography, X-Ray Computed Deep Learning Datasets as Topic Radiology Information Systems Humans Electrocardiography
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Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks.

Sensors (Basel, Switzerland) Feb 3, 2025
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...
Humans Algorithms Wearable Electronic Devices Privacy Neural Networks, Computer Machine Learning Federated Learning Internet of Things Human Activities
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Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images.

Scientific reports Jan 30, 2025
The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advi...
Machine Learning Algorithms Breast Neoplasms Early Detection of Cancer Female Humans Mammography Federated Learning
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Federated learning with bilateral defense via blockchain.

Neural networks : the official journal of the International Neural Network Society Jan 27, 2025
Federated Learning (FL) offers benefits in protecting client data privacy but also faces multiple security challenges, such as privacy breaches from unencrypted data transmission and poisoning attacks that compromise model performance, however, most ...
Federated Learning Computer Security Blockchain Humans Machine Learning Algorithms
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Out-of-Distribution Detection via outlier exposure in federated learning.

Neural networks : the official journal of the International Neural Network Society Jan 17, 2025
Among various out-of-distribution (OOD) detection methods in neural networks, outlier exposure (OE) using auxiliary data has shown to achieve practical performance. However, existing OE methods are typically assumed to run in a centralized manner, an...
Machine Learning Algorithms Federated Learning Humans Neural Networks, Computer
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Federated learning meets Bayesian neural network: Robust and uncertainty-aware distributed variational inference.

Neural networks : the official journal of the International Neural Network Society Jan 13, 2025
Federated Learning (FL) is a popular framework for data privacy protection in distributed machine learning. However, current FL faces some several problems and challenges, including the limited amount of client data and data heterogeneity. These lead...
Federated Learning Humans Bayes Theorem Machine Learning Algorithms Neural Networks, Computer Uncertainty
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