Journal
IEEE COMMUNICATIONS LETTERS
Volume 24, Issue 6, Pages 1279-1283Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2019.2921755
Keywords
Computational modeling; Blockchain; Training; Servers; Nickel; Delays; Data models; On-device machine learning; federated learning; blockchain; latency
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Funding
- Institute of Information & Communications Technology Planning & Evaluation (IITP) - Korea Government (MSIT) [2018-0-00170]
- Basic Science Research Foundation of Korea (NRF) - Ministry of Science and ICT [NRF-2017R1A2A2A05069810]
- Mobile Edge Intelligence at Scale (ELLIS) Project at the University of Oulu
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By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables on-device machine learning without any centralized training data or coordination by utilizing a consensus mechanism in blockchain. Moreover, we analyze an end-to-end latency model of BlockFL and characterize the optimal block generation rate by considering communication, computation, and consensus delays.
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