公开(公告)号：US20240135257A1

公开(公告)日：2024-04-25

申请号：US18528477

申请日：2023-12-04

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Sathyanarayanan Manamohan ,  KrishnaPrasad Lingadahalli Shastry ,  Vishesh Garg

IPC: G06N20/00

CPC classification number: G06N20/00

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.

公开(公告)号：US11966818B2

公开(公告)日：2024-04-23

申请号：US16282098

申请日：2019-02-21

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Sathyanarayanan Manamohan ,  Krishnaprasad Lingadahalli Shastry ,  Vishesh Garg

IPC: G06N20/00

CPC classification number: G06N20/00

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.

公开(公告)号：US11436692B2

公开(公告)日：2022-09-06

申请号：US16927109

申请日：2020-07-13

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Sathyanarayanan Manamohan ,  Vishesh Garg ,  Krishnaprasad Lingadahalli Shastry

IPC: H04L9/32 ,  G06N20/20 ,  G06N20/00 ,  G06Q20/38 ,  G06Q20/40 ,  G06Q30/02 ,  G06Q50/26 ,  G06F16/22 ,  G06Q30/00 ,  G06Q20/06 ,  G06F16/23 ,  G06Q40/04 ,  G06Q10/10 ,  H04L9/40

Abstract: Systems and methods are provided for leveraging blockchain technology in a swarm learning context, where nodes of a blockchain network that contribute data to training a machine learning model using their own local data can be rewarded. In order to conduct such data monetization in a fair and accurate manner, the systems and methods rely on various phases in which Merkle trees are used and corresponding Merkle roots are registered in a blockchain ledger. Moreover, any claims for a reward are challenged by peer nodes before the reward is distributed.

公开(公告)号：US20210233099A1

公开(公告)日：2021-07-29

申请号：US16927109

申请日：2020-07-13

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Sathyanarayanan Manamohan ,  Vishesh Garg ,  Krishnaprasad Lingadahalli Shastry

IPC: G06Q30/02 ,  G06Q40/04 ,  G06Q20/40 ,  G06Q10/10 ,  G06Q20/38 ,  G06Q30/00 ,  G06F16/23 ,  G06N20/00 ,  H04L29/06

Abstract: Systems and methods are provided for leveraging blockchain technology in a swarm learning context, where nodes of a blockchain network that contribute data to training a machine learning model using their own local data can be rewarded. In order to conduct such data monetization in a fair and accurate manner, the systems and methods rely on various phases in which Merkle trees are used and corresponding Merkle roots are registered in a blockchain ledger. Moreover, any claims for a reward are challenged by peer nodes before the reward is distributed.