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Blockchained On-Device Federated Learning

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arxiv 1808.03949 v2 pith:5MYTSCFE submitted 2018-08-12 cs.IT cs.NImath.IT

Hyesung Kim , Jihong Park , Mehdi Bennis , Seong-Lyun Kim This is my paper · ORCID
classification cs.ITcs.NImath.IT
keywords learningblockchainblockchainedblockflconsensusfederatedmodelon-device
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Survey of Secure Semantic Communications

    cs.CR 2025-01 conditional novelty 3.0 of 10

    A comprehensive survey of security and privacy challenges in semantic communication, categorized by the SemCom life cycle and paired with available defense technologies.

  2. Distilling On-Device Intelligence at the Network Edge

    cs.IT 2019-08 unverdicted novelty 3.0 of 10

    A review article that organizes communication-efficient and privacy-preserving on-device federated learning methods into three exchange modes and illustrates seven example frameworks.

  3. Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

    cs.NI 2019-09 unverdicted novelty 2.0 of 10

    The paper proposes a taxonomy and research roadmap for Edge Intelligence, dividing it into AI for edge and AI on edge, without presenting new empirical results.

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