US 2024/0119306 A1 · INTEROPERABLE PRIVACY-PRESERVING DISTRIBUTED MACHINE LEARNING METHOD FOR HETEROGENEOUS MULTI-CENTER DATA

Abstract

A learning system deploys, to one or more client devices, modules to be deployed in a learning environment of a respective client node. The learning environment of a respective client node may include modules for the client device (or client node) to collaborate with the central learning system and other client nodes via a distributed learning (e.g., federated learning, split learning) framework. In one embodiment, the learning system deploys an interoperable distributed learning environment for training a neural network encoder which can be used in heterogenous datasets to transform the heterogenous datasets across different institutions or entities into a common latent feature space. After training, the learning system receives data instances including a set of features and labels from different client nodes and trains a task neural network model configured to receive features in the latent space and generate an estimated label from the received data instances.

Claims20

Dependent claims

A method, comprising:

for one or more rounds:
providing, to each client node, parameters of an encoder of an autoencoder and a task neural network model for a current round, wherein the encoder is configured to receive a set of inputs and generate a feature in a latent space, and wherein the task neural network model is configured to receive the feature and generate an estimated label,
receiving, from each client node, update information for updating the parameters of the encoder and the task neural network model from the client node, wherein the update information for the encoder and the task neural network model from the client node are obtained using a subset of the client data for the current round, and
aggregating the update information of the encoder and the task neural network model received from the one or more client nodes;
receiving, from each client node, one or more feature-label pairs for one or more data instances, wherein a feature for a data instance is generated by applying the encoder for the client node to a set of inputs for the data instance;
and
training a task model using the feature-label pairs received from the one or more client nodes.

4 more elements

A non-transitory computer readable medium comprising stored instructions, the stored instructions when executed by at least one processor of one or more computing devices, cause the one or more computing devices to:

for one or more rounds:
provide, to each client node, parameters of an encoder of an autoencoder and a task neural network model for a current round, wherein the encoder is configured to receive a set of inputs and generate a feature in a latent space, and wherein the task neural network model is configured to receive the feature and generate an estimated label,
receive, from each client node, update information for updating the parameters of the encoder and the task neural network model from the client node, wherein the update information for the encoder and the task neural network model from the client node are obtained using a subset of the client data for the current round, and
aggregate the update information of the encoder and the task neural network model received from the one or more client nodes;
receive, from each client node, one or more feature-label pairs for one or more data instances, wherein a feature for a data instance is generated by applying the encoder for the client node to a set of inputs for the data instance;
and
train a task model using the feature-label pairs received from the one or more client nodes.

4 more elements

A computer system comprising:

one or more computer processors;
and
one or more computer readable mediums storing instructions that, when executed by the one or more computer processors, cause the computer system to:
for one or more rounds:
provide, to each client node, parameters of an encoder of an autoencoder and a task neural network model for a current round, wherein the encoder is configured to receive a set of inputs and generate a feature in a latent space, and wherein the task neural network model is configured to receive the feature and generate an estimated label,
receive, from each client node, update information for updating the parameters of the encoder and the task neural network model from the client node, wherein the update information for the encoder and the task neural network model from the client node are obtained using a subset of the client data for the current round, and
aggregate the update information of the encoder and the task neural network model received from the one or more client nodes;
receive, from each client node, one or more feature-label pairs for one or more data instances, wherein a feature for a data instance is generated by applying the encoder for the client node to a set of inputs for the data instance;
and
train a task model using the feature-label pairs received from the one or more client nodes.

7 more elements

Dependencies are inferred from the claim text.

Interoperable privacy-preserving distributed machine learning method for heterogeneous multi-center data

Life of this patent

Interoperable privacy-preserving distributed machine learning method for heterogeneous multi-center data

Life of this patent

Abstract

Claims20