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    FEDERATED CONTINUAL LEARNING
    2.
    发明公开
    FEDERATED CONTINUAL LEARNING 审中-公开

    公开(公告)号：US20240028947A1

    公开(公告)日：2024-01-25

    申请号：US17869095

    申请日：2022-07-20

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Giulio Zizzo ,  Ambrish Rawat ,  Naoise Holohan ,  Seshu Tirupathi

    IPC分类号： G06N20/00

    CPC分类号： G06N20/00

    摘要： The present disclosure relates to a method comprising at training system iteratively training a machine learning algorithm using current training data. The current training data comprises a local dataset of a current task and a replay dataset and may be updated for a next iteration as follows. A training dataset may be received. If the training dataset is not s shared dataset and its task is different from the current task: information representing the local dataset may be shared with other training systems, the local dataset may be added to the replay dataset, and the received training dataset may be used as the local dataset for a next iteration. In case the task is the current task: the received training dataset may be added to the local dataset. If the training dataset is a shared dataset, the received training dataset may be added to the replay dataset.

    TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK
    3.
    发明公开
    TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK 审中-公开

    公开(公告)号：US20240193428A1

    公开(公告)日：2024-06-13

    申请号：US18063813

    申请日：2022-12-09

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Ambrish Rawat ,  Killian Levacher ,  Giulio Zizzo ,  Ngoc Minh Tran

    IPC分类号： G06N3/088 ,  G06N3/045

    CPC分类号： G06N3/088 ,  G06N3/045

    摘要： A method, computer system, and computer program product are provided for training a federated generative adversarial network (GAN) using private data. The method is carried out at an aggregator system having a generator and a discriminator, wherein the aggregator system is in communication with multiple participant systems each having a local feature extractor and a local discriminator. The method includes: receiving, from a feature extractor at a participant system, a set of features for input to the discriminator at the aggregator system, wherein the features include features extracted from private data that is private to the participant system; and receiving, from one or more local discriminators of the participant systems, discriminator parameter updates to update the discriminator at the aggregator system, wherein the local discriminators are trained at the participant systems.

    VERIFICATION OF TRUSTWORTHINESS OF AGGREGATION SCHEME USED IN FEDERATED LEARNING
    4.
    发明公开
    VERIFICATION OF TRUSTWORTHINESS OF AGGREGATION SCHEME USED IN FEDERATED LEARNING 审中-公开

    公开(公告)号：US20240291633A1

    公开(公告)日：2024-08-29

    申请号：US18113219

    申请日：2023-02-23

    申请人： International Business Machines Corporation

    发明人： Giulio Zizzo ,  Stefano Braghin ,  Ambrish Rawat ,  Mark Purcell

    IPC分类号： H04L9/00 ,  G06N3/098

    CPC分类号： H04L9/008 ,  G06N3/098

    摘要： A computer-implemented method, system and computer program product for verifying the trustworthiness of an aggregation scheme utilized by an aggregator in the federated learning technique. A bit mask is received from each client used for training a machine learning algorithm using the federated learning technique. Such a bit mask contains values of ones and zeros, where a value of one indicates that the updated parameter of the global model corresponds to a parameter used by the local model trained on the client and a value of zero indicates that is not the case. These bit masks, which are encrypted, may then be combined using a homomorphic additive encryption scheme into a mask containing a matrix of values. If the mask contains a matrix of values of only the value of one, then the aggregator is deemed to be trustworthy. Otherwise, the aggregator is deemed to be untrustworthy.