Patent application EP18183265A (EP), filed 2018-07-12
Publication number: EP3564873A1
Title: SYSTEM AND METHOD OF DECENTRALIZED MACHINE LEARNING USING BLOCKCHAIN
Abstract: Decentralized machine learning to build models is performed at nodes where local training datasets are generated. A blockchain platform may be used to coordinate decentralized machine learning over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes. Rules in the form of smart contracts may enforce node participation in an iteration of model building and parameter sharing, as well as provide logic for electing a node that serves as a master node for the iteration. The master node obtains model parameters from the nodes and generates final parameters based on the obtained parameters. The master node may write its state to the distributed ledger indicating that the final parameters are available. Each node, via its copy of the distributed ledger, may discover the master node's state and obtain and apply the final parameters to its local model, thereby learning from other nodes.
IPC codes: G06N  20/    20            A I
Claims: number: 1
text: A system of decentralized machine learning via a plurality of iterations of training, the system comprising:
at least a first physical computing node of a blockchain network comprising a plurality of physical computing nodes, the first physical computing node programmed to:
number: 2
text: The system of claim 1, wherein to share the first training parameter, the first physical computing node is further programmed to:
number: 3
text: The system of claim 2, further comprising:
the master node, selected from among a set of physical computing nodes participating in the first iteration, the master node programmed to:
number: 4
text: The system of claim 3, wherein the first physical computing node is further programmed to:
number: 5
text: The system of claim 3, wherein the first physical computing node is further programmed to:
participate in a consensus decision to elect the master node from among the plurality of physical computing nodes.
number: 6
text: The system of claim 1, wherein to participate in the consensus decision to enroll a second physical computing node, the first physical computing node is further programmed to:
number: 7
text: The system of claim 6, wherein to participate in the consensus decision to enroll a second physical computing node, the first physical computing node is further programmed to:
number: 8
text: The system of claim 1, further comprising a third physical computing node in the blockchain network that did not participate in the first iteration, the third physical computing node programmed to:
number: 9
text: A method of decentralized machine learning via a plurality of iterations of training, the system comprising:
number: 10
text: The method of claim 9, wherein sharing the first training parameter comprises:
number: 11
text: The method of claim 10, further comprising:
number: 12
text: The method of claim 11, the method further comprising:
number: 13
text: The method of claim 11, wherein participating in the consensus decision to enroll a second physical computing node comprises:
number: 14
text: A non-transitory machine-readable storage medium comprising instructions executable by a processor of at least a first physical computing node of a blockchain network comprising a plurality of physical computing nodes, the instructions programming the processor to:
number: 15
text: The non-transitory machine-readable storage medium of claim 14, wherein to share the first training parameter, the instructions further program the processor to:
Office action: search_report, dated 2019-11-06
Rejection reasons: inventive_step_epc_56
Prior art cited: ref_id: US2017103468A1
ref_type: patent
severity: background
ref_id: US2013290223A1
ref_type: patent
severity: background
ref_id: - A. BESIR KURTULMUS ET AL: "Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain", CORR (ARXIV), 27 February 2018 (2018-02-27), pages 1 - 11, XP055546549
ref_type: npl
severity: background
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ref_type: npl
severity: background
ref_id: - GEORGOPOULOS LEONIDAS ET AL: "Distributed machine learning in networks by consensus", NEUROCOMPUTING, vol. 124, 8 April 2013 (2013-04-08), pages 2 - 12, XP028756393, ISSN: 0925-2312, DOI: 10.1016/J.NEUCOM.2012.12.055
ref_type: npl
severity: background
ref_id: - KEITH BONAWITZ ET AL: "Practical Secure Aggregation for Privacy Preserving Machine Learning", INTERNATIONAL ASSOCIATION FOR CRYPTOLOGIC RESEARCH,, vol. 20170405:163240, 5 April 2017 (2017-04-05), pages 1 - 20, XP061023038, DOI: 10.1145/3133956.3133982
ref_type: npl
severity: background
ref_id: US2013290223
ref_type: patent
severity: background
ref_id: US2017103468
ref_type: patent
severity: background
ref_id: - A. BESIR KURTULMUS ET AL, "Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain", CORR (ARXIV), (20180227), pages 1 - 11, XP055546549 [Y] 1,2,6-10,13-15 * page  1  - page  10, column  l * [A] 3-5,11,12
ref_type: npl
severity: obviousness
ref_id: - Jing Chen ET AL, "Algorand", (20170526), pages 1 - 75, URL: https://arxiv.org/pdf/1607.01341.pdf, (20190124), XP055547278 [Y] 1,2,6-10,13-15 * page  1  - page  31 * [A] 3-5,11,12
ref_type: npl
severity: obviousness
ref_id: - GEORGOPOULOS LEONIDAS ET AL, "Distributed machine learning in networks by consensus", NEUROCOMPUTING, (20130408), vol. 124, doi:10.1016/J.NEUCOM.2012.12.055, ISSN 0925-2312, pages 2 - 12, XP028756393 [A] 1-15 * page  2  - page  12, column  l, paragraph  4 *
ref_type: npl
severity: background
ref_id: - KEITH BONAWITZ ET AL, "Practical Secure Aggregation for Privacy Preserving Machine Learning", INTERNATIONAL ASSOCIATION FOR CRYPTOLOGIC RESEARCH,, (20170405), vol. 20170405:163240, doi:10.1145/3133956.3133982, pages 1 - 20, XP061023038 [A] 1-15 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2019-11-06)