SYSTEMS AND METHODS FOR ENHANCED FEEDBACK FOR CASCADED FEDERATED MACHINE LEARNING

    公开(公告)号:US20230019669A1

    公开(公告)日:2023-01-19

    申请号:US17784877

    申请日:2020-12-18

    IPC分类号: G06N3/04 G06F9/54

    摘要: Systems and methods are disclosed herein for enhanced feedback for cascaded federated machine learning (ML). In one embodiment, a method of operation of a server comprises, for a training epoch, receiving, from each of client device, information including a local ML model as trained at the client device and an estimated value of each parameter output by the local ML model. The method further comprises aggregating the local ML models to provide a global ML model and training a network ML model based on the estimated values of each of the parameters output by the local ML models and global data available at the server. The method further comprises providing, to each client device, information including the global ML model and feedback information related to a hidden neural network layer of the network ML model. The method further comprises repeating the process for one or more additional training epochs.