Summary of When Swarm Learning Meets Energy Series Data: a Decentralized Collaborative Learning Design Based on Blockchain, by Lei Xu et al.
When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain
by Lei Xu, Yulong Chen, Yuntian Chen, Longfeng Nie, Xuetao Wei, Liang Xue, Dongxiao Zhang
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Swarm Learning (SL) scheme offers a decentralized solution for energy data sharing, addressing security and privacy concerns by leveraging blockchain technology and smart contracts. The framework enables inter-organizational communication through node-governed devices, ensuring transparent trustworthiness and immutability of parameters on-chain. Compared to Local Learning approaches, SL demonstrates superior performance across three real-world energy series modeling scenarios while emphasizing enhanced data security and privacy over Centralized Learning and Federated Learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning can predict future energy use or production, but sharing data between organizations is tricky due to security concerns. To solve this problem, researchers suggest a new way of sharing data using blockchain technology. Instead of having one central server, many devices from different organizations work together to learn and share information securely. This method, called Swarm Learning, uses smart contracts to ensure that all devices agree on the same information. In tests, Swarm Learning worked better than other methods and kept data safer. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
