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A scalable blockchain-enabled federated learning architecture for edge computing
Shuyang Ren1, Eunsam Kim2, Choonhwa Lee3
1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.
Plos One
|August 16, 2024
Summary
This study introduces FLCoin, a novel blockchain and federated learning (FL) system for edge computing. FLCoin enhances efficiency and scalability in Internet of Things (IoT) networks by optimizing consensus processing.
Area of Science:
- * Distributed Systems and Artificial Intelligence
- * Blockchain Technology and Edge Computing
Background:
- * Existing deep learning and blockchain solutions for edge data processing often neglect the significant resource demands of blockchain consensus mechanisms within Internet of Things (IoT) environments.
- * Federated learning (FL) offers a privacy-preserving approach to distributed machine learning but requires efficient integration with underlying network infrastructures.
Purpose of the Study:
- * To propose and evaluate FLCoin, a novel system integrating blockchain and federated learning for efficient edge data processing in IoT networks.
- * To address the limitations of current blockchain-based federated learning approaches by optimizing consensus processing and reducing resource overhead.
Main Methods:
- * Development of a two-layer blockchain architecture tailored for federated learning (FL) processing.
- * Introduction of a novel committee-based consensus mechanism where committee members are elected through the FL process.
- * Experimental validation using the MNIST dataset to train a convolutional neural network (CNN) model.
Main Results:
- * FLCoin demonstrates stable communication overhead irrespective of network size, ensuring system scalability.
- * Consensus latency remained below 3 seconds even with increased participating nodes, leading to reduced overall training times.
- * Achieved a 90% reduction in communication overhead and a 35% decrease in training time cost compared to a similar system using PBFT consensus.
Conclusions:
- * FLCoin provides an efficient and scalable solution for integrating blockchain and federated learning in IoT edge networks.
- * The proposed architecture effectively minimizes resource requirements for consensus processing, making it suitable for resource-constrained IoT environments.
- * FLCoin lays a robust foundation for developing advanced intelligent IoT services through secure and efficient distributed intelligence.
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