Blockchained on-device federated learning
Kim, Hyesung; Park, Jihong; Bennis, Mehdi; Kim, Seong-Lyun (2019-06-10)
Institute of Electrical and Electronics Engineers
10.06.2019
H. Kim, J. Park, M. Bennis and S. Kim, "Blockchained On-Device Federated Learning," in IEEE Communications Letters, vol. 24, no. 6, pp. 1279-1283, June 2020, doi: 10.1109/LCOMM.2019.2921755
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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2019120946269
https://urn.fi/URN:NBN:fi-fe2019120946269
Tiivistelmä
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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