Blockchained On-Device Federated Learning

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.

• Publication year

  2018

• Venue

  IEEE Communications Letters

• Publication date

  2018-08-12

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  DOI 10.1109/LCOMM.2019.2921755arXiv 1808.03949
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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