AIMC Topic: Federated Learning

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FedPPD: Towards effective subgraph federated learning via pseudo prototype distillation.

Neural networks : the official journal of the International Neural Network Society Sep 1, 2025
Subgraph federated learning (subgraph-FL) is a distributed machine learning paradigm enabling cross-client collaborative training of graph neural networks (GNNs). However, real-world subgraph-FL scenarios often face subgraph heterogeneity problem, i....
Federated Learning Machine Learning Algorithms Neural Networks, Computer
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Self-attention fusion and adaptive continual updating for multimodal federated learning with heterogeneous data.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Federated learning (FL) enables collaborative model training without direct data sharing, facilitating knowledge exchange while ensuring data privacy. Multimodal federated learning (MFL) is particularly advantageous for decentralized multimodal data,...
Machine Learning Algorithms Attention Federated Learning Humans Neural Networks, Computer
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FedELR: When federated learning meets learning with noisy labels.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Existing research on federated learning (FL) usually assumes that training labels are of high quality for each client, which is impractical in many real-world scenarios (e.g., noisy labels by crowd-sourced annotations), leading to dramatic performanc...
Algorithms Federated Learning Neural Networks, Computer Machine Learning
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Learn the global prompt in the low-rank tensor space for heterogeneous federated learning.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Federated learning collaborates with multiple clients to train a global model, enhancing the model generalization while allowing the local data transmission-free and security. However, federated learning currently faces three intractable challenges: ...
Federated Learning Learning Humans Neural Networks, Computer Machine Learning Algorithms
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StoCFL: A stochastically clustered federated learning framework for Non-IID data with dynamic client participation.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated learning systems, these data samples are often decentralized and Non-Independently Identically Dist...
Stochastic Processes Federated Learning Algorithms Cluster Analysis Humans Neural Networks, Computer Machine Learning
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Quantum federated learning with pole-angle quantum local training and trainable measurement.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2025
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...
Algorithms Quantum Theory Neural Networks, Computer Machine Learning Federated Learning
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Federated Machine Learning Enables Risk Management and Privacy Protection in Water Quality.

Environmental science & technology Jun 3, 2025
Real-time water quality risk management in wastewater treatment plants (WWTPs) requires extensive data, and data sharing is still just a slogan due to data privacy issues. Here we show an adaptive water system federated averaging (AWSFA) framework ba...
Risk Management Machine Learning Privacy Wastewater Water Quality Federated Learning
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FedSynthCT-Brain: A federated learning framework for multi-institutional brain MRI-to-CT synthesis.

Computers in biology and medicine Jun 1, 2025
The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing t...
Tomography, X-Ray Computed Male Humans Brain Magnetic Resonance Imaging Federated Learning Deep Learning Image Processing, Computer-Assisted
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Development of time to event prediction models using federated learning.

BMC medical research methodology May 26, 2025
BACKGROUND: In a wide range of diseases, it is necessary to utilize multiple data sources to obtain enough data for model training. However, performing centralized pooling of multiple data sources, while protecting each patients' sensitive data, can ...
Algorithms Computer Simulation Humans Machine Learning Survival Analysis Proportional Hazards Models Time Factors Models, Statistical Hodgkin Disease Federated Learning
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Robust two stages federated learning for sensor based human activity recognition with label noise.

Scientific reports May 18, 2025
Federated learning is widely used for collaborative training of human activity recognition models across multiple devices with limited local data. However, label noise caused by human and time constraints during data annotation is common and severely...
Machine Learning Algorithms Human Activities Federated Learning Humans
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