Abstract
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves “data island” problems caused by data privacy concerns. However, large-scale FEL still faces following crucial challenges: (i) there lacks a secure and communication-efficient model training scheme for FEL; (2) there is no scalable and flexible FEL framework for updating local models and global model sharing (trading) management. To bridge the gaps, we first propose a blockchain-empowered secure FEL system with a hierarchical blockchain framework consisting of a main chain and subchains. This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation. A Proof-of-Verifying consensus scheme is then designed to remove low-quality model updates and manage qualified model updates in a decentralized and secure manner, thereby achieving secure FEL. To improve communication efficiency of the blockchain-empowered FEL, a gradient compression scheme is designed to generate sparse but important gradients to reduce communication overhead without compromising accuracy, and also further strengthen privacy preservation of training data. The security analysis and numerical results indicate that the proposed schemes can achieve secure, scalable, and communication-efficient decentralized FEL.
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12 November 2020
In the originally published version of the chapter 12, the funding information in the acknowledgments section was incorrect. The funding information was removed.
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Acknowledgments
This research is supported by the National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience, NRF2017EWT-EP003-041, NRF2015-NRF-ISF001-2277, Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007, A*STAR-NTU-SUTD Joint Research Grant on AI for the Future of Manufacturing RGANS1906, Wallenberg AI, Autonomous Systems and Software Program and NTU under grant M4082187 (4080), Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), NTU, Singapore, and also the National Natural Science Foundation of China (Grant 61872310) and the Shenzhen Basic Research Funding Scheme (JCYJ20170818103849343), the Open Fund of Hubei Key Laboratory of Transportation Internet of Things, China (No. WHUTIOT-2019005), and the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT20044).
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Kang, J. et al. (2020). Scalable and Communication-Efficient Decentralized Federated Edge Learning with Multi-blockchain Framework. In: Zheng, Z., Dai, HN., Fu, X., Chen, B. (eds) Blockchain and Trustworthy Systems. BlockSys 2020. Communications in Computer and Information Science, vol 1267. Springer, Singapore. https://doi.org/10.1007/978-981-15-9213-3_12
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DOI: https://doi.org/10.1007/978-981-15-9213-3_12
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