Abstract
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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866
total citations
- FWCI
- 56.29
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- 100%
- References
- 17
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Authors
4Topics & keywords
Topics
Keywords
- Computer science
- Federated learning
- Distributed learning
- Block (permutation group theory)
- Architecture
- Latency (audio)
- Computation
- Blockchain
UN Sustainable Development Goals
- Quality Education
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