利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

70 条记录 (耗时 0.022 秒)

    Semantic-aware and user-aware admission control for performance management in data analytics and data storage systems
    35.
    发明授权
    Semantic-aware and user-aware admission control for performance management in data analytics and data storage systems 有权

    公开(公告)号：US09870251B2

    公开(公告)日：2018-01-16

    申请号：US14869798

    申请日：2015-09-29

    申请人： International Business Machines Corporation

    发明人： Nathalie Baracaldo Angel ,  Heiko H. Ludwig ,  William W. Owen ,  Rui Zhang

    IPC分类号： G06F9/46

    CPC分类号： G06F9/46 ,  G06F9/4843

    摘要： In one embodiment, a computer program product includes a computer-readable storage medium having program instructions embodied therewith. The embodied program instructions are executable by a processor to cause the processor to receive, by the processor, a first job request, and analyze, by the processor, the first job request to determine: an estimated complexity of the first job request based on one or more attributes of the first job request and a user skill level of a user that submitted the first job request. Moreover, the embodied program instructions are executable by the processor to admit, by the processor, the first job request to a data analytics system and/or a data storage system in a specified order with respect to other received job requests based on at least: the estimated complexity of the first job request, and the user skill level of the user that submitted the first job request.

    Building a federated learning framework
    36.
    发明授权
    Building a federated learning framework 有权

    公开(公告)号：US12093837B2

    公开(公告)日：2024-09-17

    申请号：US16536711

    申请日：2019-08-09

    申请人： International Business Machines Corporation

    发明人： Yi Zhou ,  Rui Zhang ,  Heiko H. Ludwig ,  Jonathan F. Brunn

    IPC分类号： G06N3/10 ,  G06N3/045 ,  G06N3/08

    CPC分类号： G06N3/105 ,  G06N3/045 ,  G06N3/08

    摘要： Embodiments relate to an intelligent computer platform to build a federated learning framework including creating a hierarchy of machine learning models (MLMs). The hierarchy of MLMs has a primary MLM in a primary layer. Training the primary MLM includes capturing contributing model updates across at least one communication channel. A secondary MLM is created and logically positioned in a secondary layer of the hierarchy. The secondary MLM is operatively coupled to the primary MLM across the at least one communication channel. The created secondary MLM is initialized, including cloning weights and framework of the primary MLM into the secondary MLM, and populated with secondary data. The populated data has model updates local to the created secondary MLM. The secondary MLM is logically stored local to the secondary layer, and limits access to the secondary MLM to the secondary layer.

    Parameter sharing in federated learning
    40.
    发明授权
    Parameter sharing in federated learning 有权

    公开(公告)号：US11645582B2

    公开(公告)日：2023-05-09

    申请号：US16832809

    申请日：2020-03-27

    申请人： International Business Machines Corporation

    发明人： Shashank Rajamoni ,  Ali Anwar ,  Yi Zhou ,  Heiko H. Ludwig ,  Nathalie Baracaldo Angel

    IPC分类号： G06N20/00

    CPC分类号： G06N20/00

    摘要： One embodiment provides a method for federated learning across a plurality of data parties, comprising assigning each data party with a corresponding namespace in an object store, assigning a shared namespace in the object store, and triggering a round of federated learning by issuing a customized learning request to at least one data party. Each customized learning request issued to a data party triggers the data party to locally train a model based on training data owned by the data party and model parameters stored in the shared namespace, and upload a local model resulting from the local training to a corresponding namespace in the object store the data party is assigned with. The method further comprises retrieving, from the object store, local models uploaded to the object store during the round of federated learning, and aggregating the local models to obtain a shared model.