Qualcomm incorporated (20240104367). MODEL DECORRELATION AND SUBSPACING FOR FEDERATED LEARNING simplified abstract
Contents
- 1 MODEL DECORRELATION AND SUBSPACING FOR FEDERATED LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODEL DECORRELATION AND SUBSPACING FOR FEDERATED LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MODEL DECORRELATION AND SUBSPACING FOR FEDERATED LEARNING
Organization Name
Inventor(s)
Jamie Menjay Lin of San Diego CA (US)
Debasmit Das of San Diego CA (US)
MODEL DECORRELATION AND SUBSPACING FOR FEDERATED LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104367 titled 'MODEL DECORRELATION AND SUBSPACING FOR FEDERATED LEARNING
Simplified Explanation
The present disclosure provides techniques for training a machine learning model using federated learning. The method involves partitioning the model into multiple partitions, transmitting update requests to participating devices, receiving updates from these devices, and updating the model based on the received updates.
- Partitioning the machine learning model into multiple partitions
- Transmitting update requests to participating devices in a federated learning scheme
- Receiving updates from participating devices and updating the model accordingly
Potential Applications
This technology can be applied in various fields such as healthcare, finance, and telecommunications for training machine learning models using distributed data sources.
Problems Solved
This technology solves the problem of training machine learning models on decentralized data sources without compromising data privacy and security.
Benefits
The benefits of this technology include improved model accuracy, reduced communication costs, and enhanced data privacy.
Potential Commercial Applications
One potential commercial application of this technology is in the development of personalized recommendation systems for e-commerce platforms.
Possible Prior Art
One possible prior art for this technology is the concept of federated learning, which involves training machine learning models across multiple devices without centralizing the data.
What are the potential security risks associated with federated learning?
Federated learning involves training machine learning models on decentralized data sources, which can pose security risks such as data leakage, model poisoning attacks, and inference attacks.
How does federated learning differ from traditional centralized machine learning approaches?
Federated learning differs from traditional centralized machine learning approaches by training models on data distributed across multiple devices without sharing the raw data. This decentralized approach helps preserve data privacy and security.
Original Abstract Submitted
certain aspects of the present disclosure provide techniques and apparatus for training a machine learning model. an example method generally includes partitioning a machine learning model into a plurality of partitions. a request to update a respective partition of the plurality of partitions in the machine learning model is transmitted to each respective participating device of a plurality of participating devices in a federated learning scheme, and the request may specify that the respective partition is to be updated based on unique data at the respective participating device. updates to one or more partitions in the machine learning model are received from the plurality of participating devices, and the machine learning model is updated based on the received updates.
