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

IPC: H04L29/06 ,  H04L9/06 ,  H04L9/30 ,  H04L9/00 ,  G06F21/60 ,  G06N20/20 ,  H04L9/08

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：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

IPC: H04L9/06 ,  H04L9/30 ,  H04L9/00 ,  H04L9/08 ,  G06F21/60 ,  G06N20/20

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：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

IPC: H04L29/06 ,  H04L9/06 ,  H04L9/30 ,  H04L9/00 ,  G06F21/60 ,  G06N20/20 ,  H04L9/08

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.

公开(公告)号：US11481665B2

公开(公告)日：2022-10-25

申请号：US16186422

申请日：2018-11-09

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Satish Kumar Mopur ,  Gregory S. Battas ,  Gunalan Perumal Vijayan ,  Krishnaprasad Lingadahalli Shastry ,  Saikat Mukherjee ,  Ashutosh Agrawal ,  Sridhar Balachandriah

IPC: G06N20/00 ,  H04L67/12 ,  G06N5/04 ,  G06N3/08

Abstract: A system and method for accounting for the impact of concept drift in selecting machine learning training methods to address the identified impact. Pattern recognition is performed on performance metrics of a deployed production model in an Internet-of-Things (IoT) environment to determine the impact that concept drift (data drift) has had on prediction performance. This concurrent analysis is utilized to select one or more approaches for training machine learning models, thereby accounting for the temporal dynamics of concept drift (and its subsequent impact on prediction performance) in a faster and more efficient manner.

公开(公告)号：US12088476B2

公开(公告)日：2024-09-10

申请号：US17933934

申请日：2022-09-21

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Satish Kumar Mopur ,  Saikat Mukherjee ,  Gunalan Perumal Vijayan ,  Sridhar Balachandriah ,  Ashutosh Agrawal ,  KrishnaPrasad Lingadahalli Shastry ,  Gregory S. Battas

IPC: H04L41/16 ,  G06N20/00 ,  H04L41/0816 ,  H04L41/22 ,  H04L43/045 ,  H04L43/091 ,  H04L67/125

CPC classification number: H04L41/16 ,  G06N20/00 ,  H04L41/0816 ,  H04L41/22 ,  H04L43/045 ,  H04L43/091 ,  H04L67/125

Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.

公开(公告)号：US20230017701A1

公开(公告)日：2023-01-19

申请号：US17933934

申请日：2022-09-21

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Satish Kumar Mopur ,  Saikat Mukherjee ,  Gunalan Perumal Vijayan ,  Sridhar Balachandriah ,  Ashutosh Agrawal ,  KrishnaPrasad Lingadahalli Shastry ,  Gregory S. Battas

IPC: H04L41/16 ,  H04L43/045 ,  H04L43/091 ,  H04L67/125 ,  H04L41/22 ,  G06N20/00 ,  H04L41/0816

Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.

公开(公告)号：US11469969B2

公开(公告)日：2022-10-11

申请号：US16152394

申请日：2018-10-04

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Satish Kumar Mopur ,  Saikat Mukherjee ,  Gunalan Perumal Vijayan ,  Sridhar Balachandriah ,  Ashutosh Agrawal ,  Krishnaprasad Lingadahalli Shastry ,  Gregory S. Battas

IPC: G06F15/173 ,  H04L41/16 ,  H04L67/125 ,  H04L43/045 ,  H04L41/0816 ,  H04L41/22 ,  G06N20/00 ,  H04L43/091

Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.

公开(公告)号：US11361245B2

公开(公告)日：2022-06-14

申请号：US16100076

申请日：2018-08-09

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Satish Kumar Mopur ,  Saikat Mukherjee ,  Gunalan Perumal Vijayan ,  Sridhar Balachandriah ,  Ashutosh Agrawal ,  Krishnaprasad Lingadahalli Shastry ,  Gregory S. Battas

IPC: G06F15/16 ,  G06N20/00 ,  H04L67/12 ,  G06F9/54

Abstract: The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things (“IoT”) datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.

公开(公告)号：US20190028407A1

公开(公告)日：2019-01-24

申请号：US15654846

申请日：2017-07-20

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Gunalan Perumal Vijayan ,  Saikat Mukherjee ,  Satish Kumar Mopur ,  Sridhar Balachandriah

IPC: H04L12/911 ,  H04L12/24 ,  H04L12/26 ,  H04L29/08

Abstract: Example implementations relate to managing compliance of workloads to quality of service (QoS) parameters. An example includes collection of time-series network performance data from server systems and fabric interconnects related to traffic generated by workloads of the server systems. Rapid trends and long term trends for the workloads are calculated, using the collected network performance data as the input. Compliance of a high priority workload to an associated QoS parameter with the high priority workload is managed based on monitoring a rapid analytic trend for the high priority workload. Compliance of all of the workloads to respective QoS parameters is managed based on monitoring of long term analytic trends for the workloads.