Qualcomm incorporated (20240104384). MANAGEMENT OF FEDERATED LEARNING simplified abstract

MANAGEMENT OF FEDERATED LEARNING

Organization Name

qualcomm incorporated

Inventor(s)

Rajeev Kumar of San Diego CA (US)

Gavin Bernard Horn of La Jolla CA (US)

Aziz Gholmieh of Del Mar CA (US)

MANAGEMENT OF FEDERATED LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240104384 titled 'MANAGEMENT OF FEDERATED LEARNING

Simplified Explanation

The abstract describes methods, systems, and devices for wireless communications, specifically focusing on a federated learning procedure for training a predictive model with user equipment (UEs). The server selects UEs, determines training parameters, transmits training configurations, activates the learning procedure, aggregates model parameters, and updates the model parameter set.

• User equipment (UEs) are selected by a server for a federated learning procedure.
• The server determines training parameters for the learning procedure and transmits them to the UEs.
• The UEs locally train the predictive model according to the training configuration and report the model parameters back to the server.
• The server aggregates the reported model parameters into an updated model parameter set and assigns an updated parameter set identifier to it.
• The server informs the UEs of the updated parameter set identifier.

Potential Applications

This technology can be applied in various fields such as telecommunications, machine learning, and data analytics.

Problems Solved

This technology solves the problem of efficiently training predictive models using distributed user equipment in a wireless communication network.

Benefits

The benefits of this technology include improved model training efficiency, reduced network congestion, and enhanced predictive model accuracy.

Potential Commercial Applications

Potential commercial applications of this technology include mobile network optimization, personalized content recommendation systems, and targeted advertising platforms.

Possible Prior Art

One possible prior art could be the use of federated learning in machine learning applications to train models using distributed data sources.

Unanswered Questions

How does the system handle privacy and security concerns when transmitting model parameters between the server and user equipment?

The abstract does not provide details on the privacy and security measures implemented in the system to protect the model parameters during transmission.

What is the scalability of the system in terms of the number of user equipment that can participate in the federated learning procedure?

The abstract does not mention the scalability limitations of the system in terms of accommodating a large number of user equipment for training the predictive model.

Original Abstract Submitted

methods, systems, and devices for wireless communications are described. a server (e.g., a network entity, a model repository) may select user equipment (ues) to participate in a federated learning procedure for training a predictive model. based on selecting the ues, the server may determine a set of training parameters for a training configuration for the federated learning procedure. the server may transmit an indication of the training configuration to the ues. the server may activate the federated learning procedure by transmitting an activation indication to the ues. each ue may locally train the predictive model according to the training configuration, and may report the model parameters to the server. the server may aggregate the reported model parameters into an updated model parameter set. the server may assign an updated parameter set identifier (ps id) to the updated model parameter set and may inform the ues of the updated ps id.