-
公开(公告)号：US20210233192A1
公开(公告)日：2021-07-29
申请号：US16773397
申请日：2020-01-27
Applicant: Hewlett Packard Enterprise Development LP
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：US11887204B2
公开(公告)日：2024-01-30
申请号：US17876100
申请日：2022-07-28
Applicant: Hewlett Packard Enterprise Development LP
IPC: G06N20/20 , G06Q30/018 , G06N20/00 , G06Q10/10 , G06Q20/38 , G06Q20/40 , G06Q30/0207 , G06Q50/26 , H04L9/32 , H04L9/40 , G06Q20/06 , G06F16/23 , G06F16/22 , G06Q40/04
CPC classification number: G06Q50/265 , G06F16/2246 , G06F16/2255 , G06F16/2315 , G06F16/2365 , G06N20/00 , G06N20/20 , G06Q10/10 , G06Q20/0655 , G06Q20/388 , G06Q20/401 , G06Q30/0185 , G06Q30/0215 , G06Q40/04 , H04L9/3236 , H04L63/0435 , G06Q2220/00 , G06Q2220/10
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：US11748835B2
公开(公告)日：2023-09-05
申请号：US16773397
申请日：2020-01-27
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
IPC: H04L9/32 , G06F16/22 , G06N20/20 , G06Q20/06 , G06F16/23 , G06N20/00 , G06Q20/38 , G06Q50/26 , H04L9/40 , G06Q30/018 , G06Q10/10 , G06Q20/40 , G06Q30/0207 , G06Q40/04
CPC classification number: G06Q50/265 , G06F16/2246 , G06F16/2255 , G06F16/2315 , G06F16/2365 , G06N20/00 , G06N20/20 , G06Q10/10 , G06Q20/0655 , G06Q20/388 , G06Q20/401 , G06Q30/0185 , G06Q30/0215 , G06Q40/04 , H04L9/3236 , H04L63/0435 , G06Q2220/00 , G06Q2220/10
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：US11605013B2
公开(公告)日：2023-03-14
申请号：US16163159
申请日：2018-10-17
Applicant: Hewlett Packard Enterprise Development LP
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.
-
公开(公告)号：US11218293B2
公开(公告)日：2022-01-04
申请号：US16773555
申请日：2020-01-27
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Sathyanarayanan Manamohan , Vishesh Garg , Krishnaprasad Lingadahalli Shastry , Saikat Mukherjee
Abstract: Systems and methods are provided for implementing swarm learning while using blockchain technology and election/voting mechanisms to ensure data privacy. Nodes may train local instances of a machine learning model using local data, from which parameters are derived or extracted. Those parameters may be encrypted and persisted until a merge leader is elected that can merge the parameters using a public key generated by an external key manager. A decryptor that is not the merge leader can be elected to decrypt the merged parameter using a corresponding private key, and the decrypted merged parameter can then be shared amongst the nodes, and applied to their local models. This process can be repeated until a desired level of learning has been achieved. The public and private keys are never revealed to the same node, and may be permanently discarded after use to further ensure privacy.
-
Patent Agency Ranking
公开(公告)号：US20210234668A1
公开(公告)日：2021-07-29
申请号：US16773555
申请日：2020-01-27
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： SATHYANARAYANAN MANAMOHAN , Vishesh Garg , Krishnaprasad Lingadahalli Shastry , Saikat Mukherjee
Abstract: Systems and methods are provided for implementing swarm learning while using blockchain technology and election/voting mechanisms to ensure data privacy. Nodes may train local instances of a machine learning model using local data, from which parameters are derived or extracted. Those parameters may be encrypted and persisted until a merge leader is elected that can merge the parameters using a public key generated by an external key manager. A decryptor that is not the merge leader can be elected to decrypt the merged parameter using a corresponding private key, and the decrypted merged parameter can then be shared amongst the nodes, and applied to their local models. This process can be repeated until a desired level of learning has been achieved. The public and private keys are never revealed to the same node, and may be permanently discarded after use to further ensure privacy.
-
公开(公告)号：US11886959B2
公开(公告)日：2024-01-30
申请号：US16282098
申请日：2019-02-21
Applicant: Hewlett Packard Enterprise Development LP
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：US11876891B2
公开(公告)日：2024-01-16
申请号：US17533595
申请日：2021-11-23
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Sathyanarayanan Manamohan , Vishesh Garg , Krishnaprasad Lingadahalli Shastry , Saikat Mukherjee
CPC classification number: H04L9/0637 , G06F21/602 , G06N20/20 , H04L9/008 , H04L9/0819 , H04L9/30 , H04L9/50
Abstract: Systems and methods are provided for implementing swarm learning while using blockchain technology and election/voting mechanisms to ensure data privacy. Nodes may train local instances of a machine learning model using local data, from which parameters are derived or extracted. Those parameters may be encrypted and persisted until a merge leader is elected that can merge the parameters using a public key generated by an external key manager. A decryptor that is not the merge leader can be elected to decrypt the merged parameter using a corresponding private key, and the decrypted merged parameter can then be shared amongst the nodes, and applied to their local models. This process can be repeated until a desired level of learning has been achieved. The public and private keys are never revealed to the same node, and may be permanently discarded after use to further ensure privacy.
-
9.发明申请公开(公告)号：US20200311583A1
公开(公告)日：2020-10-01
申请号：US16372098
申请日：2019-04-01
Applicant: Hewlett Packard Enterprise Development LP
Inventor： SATHYANARAYANAN MANAMOHAN , Krishnaprasad Lingadahalli Shastry , Vishesh Garg , Eng Lim Goh
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 decentralized network. A master node on the decentralized network, can include fault tolerance features. Fault tolerance involves determining whether a number of computing nodes in a population for participating in an iteration of training is above a threshold. The master node ensures that the minimum number of computing nodes for a population, indicated by the threshold, is met before continuing with an iteration. Thus, the master node can prevent decentralized ML from continuing with an insufficient population of participating node that may impact the precision of the model and/or the overall learning ability of the decentralized ML system.
-
10.发明申请公开(公告)号：US20200272934A1
公开(公告)日：2020-08-27
申请号：US16282098
申请日：2019-02-21
Applicant: Hewlett Packard Enterprise Development LP
IPC: 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.
-
-
-
-
-
-
-
-
-
Search Results
14
records
(took 0.021 seconds)
