DETECTING AND DEFENDING AGAINST ADVERSARIAL ATTACKS IN DECENTRALIZED MACHINE LEARNING SYSTEMS

    公开(公告)号:US20240259208A1

    公开(公告)日:2024-08-01

    申请号:US18459891

    申请日:2023-09-01

    CPC classification number: H04L9/3236 G06N20/00 H04L2209/463

    Abstract: A system and a method for detecting and defending against adversarial attacks in decentralized learning models are described. The method comprises obtaining a learning parameter for determining a reference cryptographic hash value and a similarity between the data processing nodes (102). A cryptographic hash value is determined for each data processing node (102) based on the learning parameter. The trust score of each data processing node (102) is updated based on matching of the cryptographic hash value with the reference cryptographic hash value. The learning parameter of each data processing node (102) is merged to obtain a merged learning parameter based on the trust score. The merged learning parameter is provided to the data processing nodes (102) to be used for training the machine learning models.

    OPERATION EXECUTION ON MEMORY SERVERS

    公开(公告)号:US20250013386A1

    公开(公告)日:2025-01-09

    申请号:US18346406

    申请日:2023-07-03

    Abstract: In some examples, a system includes a plurality of memory servers managing access of data in a memory. A computer node includes a plurality of buffers associated with the memory servers. A processor executes a plurality of functions accessible by the computer node to access the data of the memory servers, the plurality of functions including associating, with the plurality of buffers, information specifying a type of an operation to be performed on the data using the plurality of buffers, queueing the operation in the plurality of buffers, initiating an execution of the operation, based on the type specified by the information, at the memory servers associated with the plurality of buffers, and providing results of the operation from the memory servers to the computer node.

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