US 11,886,959 B2Granted

System and method for self-healing in decentralized model building for machine learning using blockchain

AssigneeHewlett Packard Enterprise Development LP

InventorsSathyanarayanan Manamohan, Krishnaprasad Lingadahalli Shastry, Vishesh Garg

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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 (ML) over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes communicating via a blockchain network. A node can include self-healing features to recover from a fault condition within the blockchain network in manner that does not negatively impact the overall learning ability of the decentralized ML system. During self-healing, the node can determine that a local ML state is not consistent with the global ML state and trigger a corrective action to recover the local ML state. Thereafter, the node can generate a blockchain transaction indicating that it is in-sync with the most recent iteration of training, and informing other nodes to reintegrate the node into ML.

Claims20

Dependent claims

A system of decentralized machine learning (ML) comprising:

a self-healing computer node of a blockchain network comprising a plurality of computer nodes, the self-healing computer node when recovering from a fault condition within the blockchain network being programmed to:
determine, by the self-healing computer node, that the self-healing computer node is unable to share training parameters in a first iteration of training a machine-learning model;
based on the determination, generate a first blockchain transaction comprising an indication that the self-healing computer node is out-of-sync with the first iteration of training the machine-learned model, wherein the first blockchain transaction is to be added to a distributed ledger and informs the plurality of computer nodes that the self-healing computer node is not ready to participate in a second iteration of training the machine-learned model;
obtain a global ML state from the distributed ledger;
compare the obtained global ML state with a local ML state at the self-healing computer node to determine whether the local ML state is consistent with the global ML state;
based on the comparing, determine that the global ML state is not consistent with the local ML state;
in response to the determining recover the local ML state using a trigger of a corrective action;
determine, by the self-healing computer node, that the self-healing computer node is able to share the training parameters in the second iteration of training the machine-learning model;
based on the determination, generate a second blockchain transaction comprising an indication that the self-healing computer node is in-sync with the second iteration of training the machine-learned model, wherein the second blockchain transaction is to be added to the distributed ledger and informs the plurality of computer nodes that the self-healing computer node is ready to participate in the second iteration of training the machine-learned model;
and
re-enroll the self-healing computer node with the blockchain network to participate in the second iteration of training the machine-learned model.

8 more elements

A method of a self-healing computer node of a blockchain network comprising a plurality of computer nodes, the self-healing computer node recovering from a fault condition within the blockchain network, the method comprising:

determining that the self-healing computer node is unable to share training parameters in a first iteration of training a machine-learning model;
based on the determination, automatically generating, by the self-healing computer node, a first blockchain transaction comprising an indication that the self-healing computer node is out-of-sync with the first iteration of training the machine-learned model, wherein the first blockchain transaction is to be added to a distributed ledger and informs the plurality of computer nodes that the self-healing computer node is not ready to participate in a second iteration of training the machine-learned model;
obtaining, by the self-healing computer node, a global ML state from the distributed ledger;
comparing, by the self-healing computer node, the obtained global ML state with a local ML state at the self-healing computer node to determine whether the local ML state is consistent with the global ML state;
based on the comparing, determine that the global ML state is not consistent with the local ML state;
in response to the determining recover the local ML state using a trigger of a corrective action;
determining that the self-healing computer node is able to share the training parameters in the second iteration of training the machine-learning model;
based on the determination, automatically generating, by the self-healing computer node, a second blockchain transaction comprising an indication that the self-healing computer node is in-sync with the second iteration of training the machine-learned model, wherein the second blockchain transaction is to be added to the distributed ledger and informs the plurality of computer nodes that the self-healing computer node is ready to participate in the second iteration of training the machine-learned model;
and
re-enrolling, by the self-healing computer node, with the blockchain network to participate in the second iteration of training the machine-learned model.

7 more elements

A non-transitory machine-readable storage medium comprising instructions executable by a self-healing computer node of a blockchain network comprising a plurality of computer nodes, wherein when the self-healing computer node is recovering from a fault condition within the blockchain network, the instructions upon execution cause the self-healing computer node to:

determine that the self-healing computer node is unable to share training parameters in a first iteration of training a machine-learning model;
based on the determination generate a first blockchain transaction comprising an indication that the self-healing computer node is out-of-sync with the first iteration of training the machine-learned model, wherein the first blockchain transaction is to be added to a distributed ledger and informs the plurality of computer nodes that the self-healing computer node is not ready to participate in a second iteration of training the machine-learned model;
obtain a global ML state from the distributed ledger;
compare the obtained global ML state with a local ML state at the self-healing computer node to determine whether the local ML state is consistent with the global ML state;
based on the comparing, determine that the global ML state is not consistent with the local ML state;
in response to the determining recover the local ML state using a trigger of a corrective action;
determine that the self-healing computer node is able to share the training parameters in the second iteration of training the machine-learning model;
based on the determination, generate a second blockchain transaction comprising an indication that the self-healing computer node is in-sync with the second iteration of training the machine-learned model, wherein the second blockchain transaction is to be added to the distributed ledger and informs the plurality of computer nodes that the self-healing computer node is ready to participate in the second iteration of training the machine-learned model;
and
re-enroll the self-healing computer node with the blockchain network to participate in the second iteration of training the machine-learned model.

7 more elements

Dependencies are inferred from the claim text.