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    ANOMALIES AND DRIFT DETECTION IN DECENTRALIZED LEARNING ENVIRONMENTS
    2.
    发明公开
    ANOMALIES AND DRIFT DETECTION IN DECENTRALIZED LEARNING ENVIRONMENTS 审中-公开

    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.

    DATA SKEW DETECTION IN MACHINE LEARNING ENVIRONMENTS
    4.
    发明公开
    DATA SKEW DETECTION IN MACHINE LEARNING ENVIRONMENTS 审中-公开

    公开(公告)号：US20240312180A1

    公开(公告)日：2024-09-19

    申请号：US18184465

    申请日：2023-03-15

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： SATISH KUMAR MOPUR ,  GUNALAN PERUMAL VIJAYAN ,  SHOUNAK BANDOPADHYAY ,  VIJAYA SHARVANI HINDNAVIS ,  KRISHNAPRASAD LINGADAHALLI SHASTRY

    IPC: G06V10/762 ,  G06V10/26 ,  G06V10/42

    CPC classification number: G06V10/762 ,  G06V10/26 ,  G06V10/42

    Abstract: Systems and methods for preventing prediction performance degradation by detecting and extracting skews in data during both training and production environments is described herein. Feature extraction may be performed on training data during the training phase, followed by pattern analysis that assesses similarities across labeled training data sets. A reference pattern may be derived from the pattern analysis and feature extraction of the training data. Feature extraction and pattern analysis may be performed on production data during the serving phase, and a target pattern may be derived from the pattern analysis and feature extraction of the production data. The reference pattern and target pattern may be fed to a discrepancy detection functionality to detect discrepancies by using a sliding window to move the target pattern across the reference pattern to make comparisons between the patterns. The comparison may provide a quantitative skew across the training and production data.

    SYSTEM AND METHOD OF DECENTRALIZED MACHINE LEARNING USING BLOCKCHAIN
    9.
    发明申请
    SYSTEM AND METHOD OF DECENTRALIZED MACHINE LEARNING USING BLOCKCHAIN 审中-公开

    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.

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