AIMC Topic: Federated Learning

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Tackling Modality-Heterogeneous Client Drift Holistically for Heterogeneous Multimodal Federated Learning.

IEEE transactions on medical imaging Apr 3, 2025
Multimodal Federated Learning (MFL) has emerged as a collaborative paradigm for training models across decentralized devices, harnessing various data modalities to facilitate effective learning while respecting data ownership. In this realm, notably,...
Machine Learning Algorithms Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Federated Learning Brain Tomography, X-Ray Computed
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Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Computers in biology and medicine Mar 17, 2025
Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, e...
Internet of Things Federated Learning Algorithms Deep Learning Convolutional Neural Networks Bacteria Humans Neural Networks, Computer
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Toward Integrating Federated Learning With Split Learning via Spatio-Temporal Graph Framework for Brain Disease Prediction.

IEEE transactions on medical imaging Mar 17, 2025
Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, ...
Federated Learning Algorithms Brain Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Machine Learning Brain Diseases
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FedBM: Stealing knowledge from pre-trained language models for heterogeneous federated learning.

Medical image analysis Mar 7, 2025
Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model collaboratively without privacy leakage. However, current studies have show...
Machine Learning Algorithms Image Processing, Computer-Assisted Federated Learning
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A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.

Scientific reports Mar 1, 2025
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 ...
Humans Algorithms Deep Learning Privacy Federated Learning
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Communication-Efficient Hybrid Federated Learning for E-Health With Horizontal and Vertical Data Partitioning.

IEEE transactions on neural networks and learning systems Feb 28, 2025
Electronic healthcare (e-health) allows smart devices and medical institutions to collaboratively collect patients' data, which is trained by artificial intelligence (AI) technologies to help doctors make diagnosis. By allowing multiple devices to tr...
Telemedicine Federated Learning Neural Networks, Computer Artificial Intelligence Algorithms Communication Machine Learning Humans
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A Novel Framework for Quantum-Enhanced Federated Learning with Edge Computing for Advanced Pain Assessment Using ECG Signals via Continuous Wavelet Transform Images.

Sensors (Basel, Switzerland) Feb 26, 2025
Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preservin...
Wavelet Analysis Electrocardiography Machine Learning Signal Processing, Computer-Assisted Humans Pain Measurement Algorithms Federated Learning Pain Neural Networks, Computer
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Replica tree-based federated learning using limited data.

Neural networks : the official journal of the International Neural Network Society Feb 22, 2025
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...
Humans Deep Learning Neural Networks, Computer Machine Learning Federated Learning Algorithms
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From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare.

Medical image analysis Feb 14, 2025
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...
Computer Security Federated Learning Machine Learning Humans Delivery of Health Care
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Vertical federated learning based on data subset representation for healthcare application.

Computer methods and programs in biomedicine Feb 12, 2025
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...
Humans Artificial Intelligence Machine Learning Algorithms Federated Learning Delivery of Health Care Confidentiality
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