Qualcomm incorporated (20240104420). ACCURATE AND EFFICIENT INFERENCE IN MULTI-DEVICE ENVIRONMENTS simplified abstract

ACCURATE AND EFFICIENT INFERENCE IN MULTI-DEVICE ENVIRONMENTS

Organization Name

qualcomm incorporated

Inventor(s)

Kyu Woong Hwang of Daejeon (KR)

Seunghan Yang of Incheon (KR)

Hyunsin Park of Gwangmyeong (KR)

Leonid Sheynblat of Hillsborough CA (US)

Vinesh Sukumar of Fremont CA (US)

Ziad Asghar of San Diego CA (US)

Justin Mcgloin of Los Altos CA (US)

Joel Linsky of San Diego CA (US)

Tong Tang of Escondido CA (US)

ACCURATE AND EFFICIENT INFERENCE IN MULTI-DEVICE ENVIRONMENTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240104420 titled 'ACCURATE AND EFFICIENT INFERENCE IN MULTI-DEVICE ENVIRONMENTS

Simplified Explanation

The present disclosure relates to techniques for training and using machine learning models in multi-device network environments.

• The method involves extracting a feature set from a data set associated with a client device using a client-device-specific feature extractor.
• The feature set is a subset of features in a common feature space.
• A task-specific model is trained based on the extracted feature set and feature sets associated with other client devices.
• The feature sets of other client devices are subsets of features in the common feature space.
• A respective version of the task-specific model is deployed to each client device in a network.

Potential Applications

This technology can be applied in various fields such as:

• Internet of Things (IoT) devices
• Personalized recommendations
• Network optimization

Problems Solved

This technology addresses issues such as:

• Efficient utilization of network resources
• Personalized user experiences
• Seamless integration of machine learning models across devices

Benefits

The benefits of this technology include:

• Improved performance of machine learning models
• Enhanced user satisfaction
• Scalability in multi-device environments

Potential Commercial Applications

This technology can be commercially benefit industries such as:

• E-commerce for personalized recommendations
• Telecommunications for network optimization
• Healthcare for personalized treatment recommendations

Possible Prior Art

One possible prior art in this field is the use of federated learning techniques for training machine learning models across multiple devices.

What are the potential security implications of deploying different versions of the task-specific model to multiple client devices?

There may be security concerns related to the deployment of different versions of the task-specific model to multiple client devices, such as:

• Ensuring data privacy and confidentiality
• Preventing unauthorized access to sensitive information

How can the performance of the task-specific model be optimized for different client devices in a network environment?

The performance of the task-specific model can be optimized for different client devices in a network environment by:

• Customizing the feature extraction process for each client device
• Regularly updating and fine-tuning the model based on feedback from client devices

Original Abstract Submitted

certain aspects of the present disclosure provide techniques and apparatus for a training and using machine learning models in multi-device network environments. an example computer-implemented method for network communications performed by a host device includes extracting a feature set from a data set associated with a client device using a client-device-specific feature extractor, wherein the feature set comprises a subset of features in a common feature space, training a task-specific model based on the extracted feature set and one or more other feature sets associated with other client devices, wherein the feature sets associated with the other client devices comprise one or more subsets of features in the common feature space, and deploying, to each respective client device of a plurality of client devices, a respective version of the task-specific model.