18238998. CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION simplified abstract (QUALCOMM Incorporated)

CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION

Organization Name

QUALCOMM Incorporated

Inventor(s)

Seunghan Yang of Incheon (KR)

Seokeon Choi of Yongin-si (KR)

Hyunsin Park of Gwangmyeon (KR)

Sungha Choi of Goyang-si (KR)

Sungrack Yun of Seongnam (KR)

CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18238998 titled 'CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION

Simplified Explanation

The abstract describes a patent application for implementing federated learning by a federated client device, where client invariant information of a neural network is identified and transmitted to a federated server to generate a machine learning model for a task in a domain unknown to the server.

• Explanation of the patent:

 * Federated client device implements federated learning
 * Identifies client invariant information of a neural network
 * Transmits information to federated server
 * Server generates a machine learning model for a task in an unknown domain

Potential Applications

The technology can be applied in various fields such as healthcare, finance, and manufacturing for collaborative machine learning tasks across different domains.

Problems Solved

1. Overcoming data privacy concerns by keeping sensitive information on client devices. 2. Enabling collaborative machine learning in decentralized environments.

Benefits

1. Improved data privacy and security. 2. Enhanced model performance through collaborative learning. 3. Scalability and efficiency in machine learning tasks.

Potential Commercial Applications

Optimizing personalized recommendations in e-commerce platforms using federated learning.

Possible Prior Art

One possible prior art is the use of federated learning in mobile devices for training machine learning models without sharing raw data.

What is the impact of this technology on data privacy in machine learning tasks?

This technology enhances data privacy by allowing the training of machine learning models without sharing raw data, thus reducing the risk of data breaches and privacy violations.

How does federated learning improve model performance compared to traditional centralized approaches?

Federated learning enables collaborative model training across multiple devices, leading to more diverse and representative datasets, which can result in improved model performance and generalization.

Original Abstract Submitted

Example implementations include methods, apparatuses, and computer-readable mediums of federated learning by a federated client device, comprising identifying client invariant information of a neural network for performing a machine learning (ML) task in a first domain known to a federated server. The implementations further comprising transmitting the client invariant information to the federated server, the federated server configured to generate a ML model for performing the ML task in a domain unknown to the federated server based on the client invariant information and other client invariant information of another neural network for performing the ML task in a second domain known to the federated server.