ZERO PEEK ROBUSTNESS CHECKS FOR FEDERATED LEARNING

    公开(公告)号:US20240346379A1

    公开(公告)日:2024-10-17

    申请号:US18633905

    申请日:2024-04-12

    CPC classification number: G06N20/00

    Abstract: A computing system determines a median of a first number of mean values received from a first number of clusters, where each cluster of the first number of clients includes a first plurality of clients. Also, a threshold is determined based on the median, where the threshold applies to model updates. The median and the threshold are broadcast to all clients. Next, one or more clients that fail to provide a proof attesting that their model update is within the threshold of the median are dropped. Then, a second plurality of clients, not including the one or more dropped clients, participate in a final round of secure aggregation. Next, a final aggregate result is obtained, where the final aggregate result is based on the final round of secure aggregation. Then, one or more actions are performed based on the final aggregate result.

    QUANTIZATION AND CRYPTOGRAPHIC PROTOCOL BASED MACHINE LEARNING MODELS FOR CONFIDENTIAL DATA ANALYSIS AND INFERENCE

    公开(公告)号:US20250005200A1

    公开(公告)日:2025-01-02

    申请号:US18708339

    申请日:2022-11-08

    Abstract: In some embodiments, there is provided a system, which comprises a processor, and at least one non-transitory computer readable media storing instructions. The stored instructions, when executed by the processor, cause the processor to perform operations comprising initiating a cryptographic protocol between a first computing environment and a second computing environment, the initiating including: securing, in association with the second computing environment, content associated with data of a user associated with the first computing environment, and securing, in association with the first computing environment, a parameter associated with a trained machine learning model, implementing the trained machine learning model on the data that is secured, the machine learning model operating on the first computing environment and the second computing environment, determining an output associated with the data that is secured, responsive to the implementing of the trained machine learning model, and providing the output to the first computing environment.

    Small-world nets for fast neural network training and execution

    公开(公告)号:US11625614B2

    公开(公告)日:2023-04-11

    申请号:US16661226

    申请日:2019-10-23

    Abstract: A method, a system, and a computer program product for fast training and/or execution of neural networks. A description of a neural network architecture is received. Based on the received description, a graph representation of the neural network architecture is generated. The graph representation includes one or more nodes connected by one or more connections. At least one connection is modified. Based on the generated graph representation, a new graph representation is generated using the modified at least one connection. The new graph representation has a small-world property. The new graph representation is transformed into a new neural network architecture.

Patent Agency Ranking