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公开(公告)号：US20240028417A1
公开(公告)日：2024-01-25
申请号：US17868587
申请日：2022-07-19
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： SATHYANARAYANAN MANAMOHAN , SATISH KUMAR MOPUR , KRISHNAPRASAD LINGADAHALLI SHASTRY , RAVI SARVESWARA
CPC classification number: G06F9/5077 , H04L9/50 , G06F9/45558 , G06N20/20
Abstract: Systems and methods provide for a federated workflow solution to orchestrate entire machine learning (ML) workflows comprising multiple tasks, across silos. In other words, one or more sets/pluralities of tasks making up an ML workflow, can be executed across multiple resource partitions or domains. Federated workflow state can be maintained and shared through some form of distributed database/ledger, such as a blockchain. Agents that are locally deployed locally at the silos may orchestrate an ML workflow at a particular resource domains, each such agent having access, via the blockchain (acting as a globally visible/consistent state store), to the aforementioned workflow state. Such systems are capable of operating regardless of the existence of heterogeneous resources/aspects of a silo.
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2.发明申请公开(公告)号：US20230138780A1
公开(公告)日：2023-05-04
申请号：US17515438
申请日：2021-10-30
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Abstract: Systems and methods are provided for machine learning in a distributed, privacy-preserving manner. Particularly, the decentralized system can share machine learning models in a protected manner by training a first sub-model with a first local data set at a first node and obfuscating the trained first sub-model as a first obfuscated sub-model. The model may be shared with a second node, that can construct a local instance of a stacked ensemble comprising the first obfuscated sub-model and a trainable parametric layer and train the local instance of the stacked ensemble with a second local data set accessible locally at the second node.
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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.
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公开(公告)号：US20240256525A1
公开(公告)日：2024-08-01
申请号：US18160704
申请日：2023-01-27
Applicant: Hewlett Packard Enterprise Development LP
Inventor： SATHYANARAYANAN MANAMOHAN , KRISHNAPRASAD LINGADAHALLI SHASTRY , RAVI SARVESWARA , SAIKAT MUKHERJEE
CPC classification number: G06F16/2379 , G06F16/273 , G06F21/6218
Abstract: Systems and methods are disclosed for providing decentralized policy-based transactional object management for systems employing federated workflows. Various disclosed components may be added to one or more nodes of a decentralized network, wherein the disclosed components perform registration, replication, and read/write access interfacing functions. These functions result in the storage of objects on the decentralized network in a way which allows for decentralized, policy-based, and transactional management of the objects.
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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
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.
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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.
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公开(公告)号：US20190332702A1
公开(公告)日：2019-10-31
申请号：US16171288
申请日：2018-10-25
Applicant: Hewlett Packard Enterprise Development LP
Inventor： SATHYANARAYANAN MANAMOHAN , KRISHNAPRASAD LINGADAHALLI SHASTRY , AVINASH CHANDRA PANDEY , RAVI SARVESWARA
Abstract: The disclosure relates to decentralized management of nodes in a blockchain network. Participants may agree to a consensus rules and implement them as smart contracts. For example, one rule may specify that a node will accept a change proposal only when its local policies and/or data allow it to implement the change. A smart contract may implement this rule and deploy it across the blockchain network for each node to follow. Other participants, through their nodes, may propose changes to the blockchain network, and each node may consult its copy of the smart contract to determine whether to vote to approve the change request and apply the change request locally.
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公开(公告)号：US20240160939A1
公开(公告)日：2024-05-16
申请号：US17987518
申请日：2022-11-15
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： SATISH KUMAR MOPUR , KRISHNAPRASAD LINGADAHALLI SHASTRY , SATHYANARAYANAN MANAMOHAN , RAVI SARVESWARA , GUNALAN PERUMAL VIJAYAN
IPC: G06N3/08
CPC classification number: G06N3/088
Abstract: Anomalies and drift detection in decentralized learning environments. The method includes deploying at a first node, (1) a local unsupervised autoencoder, trained at the first node, along with a local training data reference baseline for the first node, and (2) a global unsupervised autoencoder trained across a plurality of nodes, along with a corresponding global training data reference baseline. Production data at the first node is processed with local and global ML models deployed by a user. At least one of local and global anomaly data regarding anomalous production data or local and global drift data regarding drifting production data is derived based on the local and global training data reference baselines, respectively. At least one of the local anomaly data is compared with the global anomaly data or the local drift data with the global drift data for assessing impact of anomalies/drift on the ML models.
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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.
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10.发明申请公开(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
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.
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