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    FEDERATED LEARNING IN COMPUTER SYSTEMS
    2.
    发明申请
    FEDERATED LEARNING IN COMPUTER SYSTEMS 有权

    公开(公告)号：US20230031052A1

    公开(公告)日：2023-02-02

    申请号：US17443840

    申请日：2021-07-28

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Jordan McAfoose ,  Adelmo Cristiano Innocenza Malossi ,  Mathieu Sinn

    IPC分类号： G06N20/20 ,  G06N5/04 ,  G06F11/34

    摘要： Methods and systems are provided for federated learning among a federation of machine learning models in a computer system. Such a method includes, in at least one node computer of the system, deploying a federation model for inference on local input data samples at the node computer to obtain an inference output for each data sample, and providing the inference outputs for use as inference results at the node computer. The method further comprises, in the system, for at least a portion of the local input data samples, obtaining an inference output corresponding to each local input data sample from at least a subset of other federation models, and using the inference outputs from the federation models to provide a standardized inference output corresponding to an input data sample at the node computer for assessing performance of the model deployed at that computer.

    Preserving inter-party data privacy in global data relationships
    3.
    发明授权
    Preserving inter-party data privacy in global data relationships 有权

    公开(公告)号：US11569985B2

    公开(公告)日：2023-01-31

    申请号：US17362143

    申请日：2021-06-29

    申请人： International Business Machines Corporation

    发明人： Ngoc Minh Tran ,  Mathieu Sinn ,  Stefano Braghin

    IPC分类号： H04L9/32 ,  H04L9/08 ,  H04L9/00 ,  G06F16/28 ,  H04L9/14

    摘要： Disclosed are techniques for determining data relationships between privacy-restricted datapoints, sourced over a computer network, which require data privacy measures concealing at least some datapoints from other clients in the network that the datapoint respectively do not originate from. A first client encrypts a first datapoint with a public key of a public/private encryption scheme and communicates it to the second client along with the public key. The second client encrypts a corresponding second datapoint with the public key, then determines a relationship between the two encrypted datapoints, and communicates the determined relationship to a central client along with the public key. Random noise is encrypted by the central client and added to the determined relationship, then sent together to the first client, followed by decryption by the first client using the private key. The central client extracts the random noise after receiving the decrypted determined relationship.

    USING A DEEP LEARNING BASED SURROGATE MODEL IN A SIMULATION
    4.
    发明申请
    USING A DEEP LEARNING BASED SURROGATE MODEL IN A SIMULATION 有权

    公开(公告)号：US20220180174A1

    公开(公告)日：2022-06-09

    申请号：US17114436

    申请日：2020-12-07

    申请人： International Business Machines Corporation

    发明人： Ambrish Rawat ,  Fearghal O'Donncha ,  Mathieu Sinn ,  Sean A. McKenna

    IPC分类号： G06N3/08 ,  G06F30/27

    摘要： A computer-implemented method, a computer program product, and a computer system for optimally balancing deployment of a deep learning based surrogate model and a physics based mathematical model in simulating a complex problem. One or more computing devices or servers compare results of running the deep learning based surrogate model with results of partially running the physics based mathematical model or with observations. One or more computing devices or severs output the results of running the deep learning based surrogate model as system outputs of simulating the complex problem, in response to determining that the deep learning based surrogate model is reliable. One or more computing devices or servers output results of running the physics based mathematical model as the system outputs of simulating the complex problem, in response to determining that the deep learning based surrogate model is not reliable.

    Protecting a machine learning model
    5.
    发明授权
    Protecting a machine learning model 有权

    公开(公告)号：US11036857B2

    公开(公告)日：2021-06-15

    申请号：US16192787

    申请日：2018-11-15

    申请人： International Business Machines Corporation

    发明人： Ngoc Minh Tran ,  Mathieu Sinn ,  Ambrish Rawat ,  Maria-Irina Nicolae ,  Martin Wistuba

    IPC分类号： G06F21/55 ,  G06F21/56 ,  H04L29/06 ,  G06N3/08 ,  G06F30/27

    摘要： A method for protecting a machine learning model includes: generating a first adversarial example by modifying an original input using an attack tactic, wherein the model accurately classifies the original input but does not accurately classify at least the first adversarial example; training a defender to protect the model from the first adversarial example by updating a strategy of the defender based on predictive results from classifying the first adversarial example; updating the attack tactic based on the predictive results from classifying the first adversarial example; generating a second adversarial example by modifying the original input using the updated attack tactic, wherein the trained defender does not protect the model from the second adversarial example; and training the defender to protect the model from the second adversarial example by updating the at least one strategy of the defender based on results obtained from classifying the second adversarial example.

    AUTOMATED DEEP LEARNING ARCHITECTURE SELECTION FOR TIME SERIES PREDICTION WITH USER INTERACTION
    10.
    发明申请
    AUTOMATED DEEP LEARNING ARCHITECTURE SELECTION FOR TIME SERIES PREDICTION WITH USER INTERACTION 有权

    公开(公告)号：US20220172038A1

    公开(公告)日：2022-06-02

    申请号：US17106966

    申请日：2020-11-30

    申请人： International Business Machines Corporation

    发明人： Bei Chen ,  Dakuo Wang ,  Martin Wistuba ,  Beat Buesser ,  Long VU ,  Chuang Gan ,  Mathieu Sinn

    IPC分类号： G06N3/08 ,  G06N3/04

    摘要： A system and method for automatically generating deep neural network architectures for time series prediction. The system includes a processor for: receiving a prediction context associated with a current use case; based on the associated prediction context, selecting a prediction model network configured for a current use case time series prediction task; replicating the selected prediction model network to create a plurality of candidate prediction model networks; inputting a time series data to each of the plurality of the candidate prediction model network; train, in parallel, each respective candidate prediction model network of the plurality with the input time series data; modifying each of the plurality of the candidate prediction model network by applying a respective different set of one or more model parameters while being trained in parallel; and determine a fittest modified prediction model network for solving the current use case time series prediction task.