A Calibrated Ensemble Algorithm to Address Data Heterogeneity in Machine Learning: An Application to Identify Severe SLE Flares in Lupus Patients
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3149477
Abstract: Motivated to address the inconsistency between the essential i.i.d. assumption in machine learning theory and the data heterogeneity in real-world applications, we propose a novel calibrated ensemble (CE) algorithm to facilitate learning with diverse data… read more here.
Keywords: calibrated ensemble; machine; data heterogeneity; machine learning ... See more keywords
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3161943
Abstract: Nowadays, the Industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect… read more here.
Keywords: data heterogeneity; federated learning; heterogeneity robust; learning via ... See more keywords
FlocOff: Data Heterogeneity Resilient Federated Learning With Communication-Efficient Edge Offloading
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2024.3431526
Abstract: Federated Learning (FL) has emerged as a fundamental learning paradigm to harness massive data scattered at geo-distributed edge devices in a privacy-preserving way. Given the heterogeneous deployment of edge devices, however, their data are usually… read more here.
Keywords: flocoff; data heterogeneity; edge; federated learning ... See more keywords
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on medical imaging"
DOI: 10.1109/tmi.2022.3233574
Abstract: The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables… read more here.
Keywords: data heterogeneity; federated learning; self supervised; medical imaging ... See more keywords
Understanding and Mitigating Dimensional Collapse in Federated Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2023.3338063
Abstract: Federated learning aims to train models collaboratively across different clients without sharing data for privacy considerations. However, one major challenge for this learning paradigm is the data heterogeneity problem, which refers to the discrepancies between… read more here.
Keywords: collapse federated; data heterogeneity; collapse; dimensional collapse ... See more keywords
Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity
Sign Up to like & getrecommendations! Published in 2022 at "Biomolecules"
DOI: 10.3390/biom12050628
Abstract: Single-particle cryogenic electron microscopy (cryo-EM) has become an indispensable tool to probe high-resolution structural detail of biomolecules. It enables direct visualization of the biomolecules and opens a possibility for averaging molecular images to reconstruct a… read more here.
Keywords: probing structural; heterogeneity; structural perturbation; data heterogeneity ... See more keywords