Qualcomm incorporated (20240112039). CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION simplified abstract

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 20240112039 titled 'CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION

Simplified Explanation

The patent application describes a method for federated learning by a 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 an unknown domain.

• Federated learning method by a client device:

 * Identify client invariant information of a neural network for a machine learning task in a known domain.
 * Transmit the client invariant information to a federated server.
 * Federated server generates a machine learning model for the task in an unknown domain based on the client invariant information and other client invariant information from another neural network in a different domain.

Potential Applications

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

Problems Solved

1. Overcoming data privacy concerns by allowing model training without sharing raw data. 2. Enabling efficient machine learning in decentralized environments with multiple client devices.

Benefits

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

Potential Commercial Applications

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

Possible Prior Art

One potential prior art could be the concept of distributed machine learning where models are trained across multiple devices without sharing raw data.

How does this technology ensure data privacy during the federated learning process?

The technology uses client invariant information to generate machine learning models without sharing raw data, thus preserving data privacy.

What are the computational requirements for implementing federated learning on client devices?

The computational requirements may vary depending on the complexity of the machine learning task and the number of client devices involved in the federated learning process.

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.