公开(公告)号：US20200272945A1

公开(公告)日：2020-08-27

申请号：US16281410

申请日：2019-02-21

Applicant: Hewlett Packard Enterprise Development LP

Inventor： SATHYANARAYANAN MANAMOHAN ,  KRISHNAPRASAD LINGADAHALLI SHASTRY ,  VISHESH GARG ,  ENG LIM GOH

IPC: G06N20/10 ,  H04L9/06 ,  G06F16/27

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 communicating via a blockchain network. A node can have a local training dataset that includes raw data, where the raw data is accessible locally at the computing node. Further, a node can train a local model based on the local training dataset during a first iteration of training a machine-learned model. The node can generate shared training parameters based on the local model in a manner that precludes any requirement for the raw data to be accessible by each of the other nodes on the blockchain network to perform the decentralized machine learning, while preserving privacy of the raw data.

公开(公告)号：US20190332955A1

公开(公告)日：2019-10-31

申请号：US16163159

申请日：2018-10-17

Applicant: Hewlett Packard Enterprise Development LP

Inventor： SATHYANARAYANAN MANAMOHAN ,  KRISHNAPRASAD LINGADAHALLI SHASTRY ,  VISHESH GARG

IPC: G06N5/04 ,  G06N99/00 ,  G06K9/62 ,  H04L9/06

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.