公开(公告)号：US20230359907A1

公开(公告)日：2023-11-09

申请号：US17848947

申请日：2022-07-01

Applicant: GOOGLE LLC

Inventor： Sean Augenstein ,  Andrew Hard ,  Kurt Partridge ,  Rajiv Mathews ,  Lin Ning ,  Karan Singhal

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: Implementations disclosed herein are directed to various techniques for mitigating and/or preventing catastrophic forgetting in federated learning of global machine learning (ML) models. Implementations may identify a global ML model that is initially trained at a remote server based on a server data set, determine server-based data for global weight(s) of the global ML model, and transmit the global ML model and the server-based data to a plurality of client devices. The server-based data may include, for example, EWC loss term(s), client augmenting gradients, server augmenting gradients, and/or server-based data. Further, the plurality client devices may generate, based on processing corresponding predicted output and using the global ML model, and based on the server-based data, a corresponding client gradient, and transmit the corresponding client gradient to the remote server. Implementations may further generate an updated global ML model based on at least the corresponding client gradients.

公开(公告)号：US20250077871A1

公开(公告)日：2025-03-06

申请号：US18564160

申请日：2023-05-25

Applicant: Google LLC

Inventor： Devora Berlowitz ,  Steve Shaw-Tang Chien ,  Yunqi Xue ,  Lin Ning ,  Shuang Song ,  Mei Chen

IPC: G06N3/084

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for privacy-sensitive training of a neural network. In one aspect, a system comprises a central memory configured to store current values of a set of neural network parameters and one or more computers that are configured to implement a plurality of worker computing units, where each worker computing unit is configured to repeatedly perform operations comprising obtaining current values of the set of neural network parameters from the central memory, sampling a batch of network inputs from a set of training data, determining a respective gradient corresponding to each network input, determining an aggregated gradient based on the gradients, identifying a subset of a set of gradient values as target values, generating a noisy gradient by combining random noise with the target gradient values, and updating the current values of the set of neural network parameters.