Results 121 to 130 of about 72,357 (237)

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

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

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 ...
Tran, Dung T., Ha, Nguyen B., Nguyen, Van-Dinh, Wong, Kok-Seng   +3 more
openaire   +2 more sources

Federated Learning Meets Blockchain in Decentralized Data Sharing: Healthcare Use Case

IEEE Internet of Things Journal
In the era of data-driven healthcare, the amalgamation of blockchain and federated learning (FL) introduces a paradigm shift toward secure, collaborative, and patient-centric data sharing. This article pioneers the exploration of the conceptual framework
S. Alsamhi, Raushan Rose Myrzashova, Ammar Hawbani, Santosh Kumar, Sumit Srivastava, Liang Zhao, Xi Wei, Mohsen Guizan, Edward Curry   +8 more
semanticscholar   +1 more source

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning

Real-world \underline{F}ederated \underline{L}earning systems often encounter \underline{D}ynamic clients with \underline{A}gnostic and highly heterogeneous data distributions (DAFL), which pose challenges for efficient communication and model initialization. To address these challenges, we draw inspiration from the recently proposed Learngene paradigm,
Guo, Shunxin, Lv, Jiaqi, Wang, Qiufeng, Geng, Xin   +3 more
openaire   +2 more sources

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

Secure Federated Evolutionary Optimization—A Survey

Engineering
With the development of edge devices and cloud computing, the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past decade.
Qiqi Liu, Yuping Yan, Yaochu Jin, Xilu Wang, Peter Ligeti, Guo Yu, Xueming Yan   +6 more
doaj   +1 more source

Federated Machine Learning: Privacy, Explainability, and Performance [PDF]

There is an increasing need for explainable and private machine learning. The European Union’s AI Act is a recent legislation aimed at regulating the development and use of artificial intelligence in the European Union.
Skribeland, Halvor
core  

WW-FL: Secure and Private Large-Scale Federated Learning

Transactions on Cryptographic Hardware and Embedded Systems
Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices.
Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle, Christian Weinert, Hossein Yalame   +5 more
doaj   +1 more source