Results 101 to 110 of about 4,684 (203)

Framework for co-distillation driven federated learning to address class imbalance in healthcare

Discover Data
Federated Learning (FL) is a pioneering approach in distributed machine learning, enabling collaborative model training across multiple clients while retaining data privacy.
Suraj Racha, Shubh Gupta, Humaira Firdowse, Aastik Solanki, Ganesh Ramakrishnan, Kshitij S. Jadhav   +5 more
doaj   +1 more source

Federated learning for critical electrical infrastructure—handling data heterogeneity for predictive maintenance of substation equipment

Frontiers in Artificial Intelligence
High-voltage substations form the backbone of critical electrical infrastructure, making predictive maintenance essential for ensuring grid resilience and operational reliability.
Soham Ghosh, Gaurav Mittal
doaj   +1 more source

Federated Learning for Soil Moisture Prediction: Benchmarking Lightweight CNNs and Robustness in Distributed Agricultural IoT Networks

Machine Learning and Knowledge Extraction
Federated learning (FL) provides a privacy-preserving approach for training machine learning models across distributed datasets; however, its deployment in environmental monitoring remains underexplored.
Salma Zakzouk, Lobna A. Said
doaj   +1 more source

Quantization-based chained privacy-preserving federated learning

Scientific Reports
Federated Learning (FL) is an advanced distributed machine learning framework crucial in protecting data privacy and security. By enabling multiple participants to train models while keeping their data local collaboratively, FL effectively mitigates the ...
Ya Liu, Shumin Wu, Yibo Li, Fengyu Zhao, Yanli Ren   +4 more
doaj   +1 more source

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning

CoRR
Federated learning (FL) is a promising approach for addressing scalability and latency issues in large-scale networks by enabling collaborative model training without requiring the sharing of raw data. However, existing FL frameworks often overlook the computational heterogeneity of edge clients and the growing training burden on resource-limited ...
Dung T. Tran, Nguyen B. Ha, Van-Dinh Nguyen, Kok-Seng Wong   +3 more
openaire   +2 more sources

Enhanced federated learning for secure medical data collaboration

Journal of Analytical Science and Technology
Federated learning (FL) enables collaborative model training across multiple institutions while preserving data privacy. However, conventional encryption techniques used in FL remain vulnerable to quantum attacks, raising concerns about the security of ...
Benjamin Appiah, Isaac Osei, Bill K. Frimpong, Daniel Commey, Kwabena Owusu-Agymang, Gabriel Assamah   +5 more
doaj   +1 more source

Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review. [PDF]

Front Digit Health
Bhardwaj T, Sumangali K.
europepmc   +1 more source

Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness. [PDF]

Front Cardiovasc Med
Li J, Xiang W, Shang D, Li S, Li Q.
europepmc   +1 more source

Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions. [PDF]

J Imaging
Ciobotaru A, Corches C, Gota D, Miclea L.   +3 more
europepmc   +1 more source

FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data. [PDF]

PLoS One
Moniruzzaman M, Uddin MS, Ahmed A, Aktarujjaman M, Ahmed M, Rahman A.   +5 more
europepmc   +1 more source