Meta platforms technologies, llc (20240112008). Active Federated Learning for Assistant Systems simplified abstract
Contents
- 1 Active Federated Learning for Assistant Systems
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Active Federated Learning for Assistant Systems - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
Active Federated Learning for Assistant Systems
Organization Name
meta platforms technologies, llc
Inventor(s)
Kshitiz Malik of Palo Alto CA (US)
Seungwhan Moon of Seattle WA (US)
Honglei Liu of San Mateo CA (US)
Anuj Kumar of Santa Clara CA (US)
Hongyuan Zhan of Seattle WA (US)
Active Federated Learning for Assistant Systems - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112008 titled 'Active Federated Learning for Assistant Systems
Simplified Explanation
The patent application describes a method for training a neural network model on multiple examples to generate updated model parameters, calculating a user valuation representing the utility of the training process, and sending the trained model and user valuation to remote servers for potential future training.
- Receiving current version of neural network model from remote servers
- Training neural network model on multiple examples from local data store
- Calculating user valuation based on utility of training process
- Sending trained model and user valuation to remote servers for potential future training
Potential Applications
The technology described in the patent application could be applied in various fields such as:
- Machine learning
- Artificial intelligence
- Data analysis
Problems Solved
This technology helps in:
- Improving the efficiency of training neural network models
- Enhancing the accuracy of model predictions
- Streamlining the process of updating model parameters
Benefits
The benefits of this technology include:
- Faster training of neural network models
- More accurate predictions
- Improved user experience
Potential Commercial Applications
The technology could be utilized in:
- Predictive analytics software
- Image recognition systems
- Natural language processing applications
Possible Prior Art
One possible prior art for this technology could be:
- Gradient descent optimization algorithms used in machine learning
Unanswered Questions
How does the user valuation impact the selection process for subsequent training of the neural network model?
The user valuation is associated with the likelihood of the first client system being chosen for future training, but the exact mechanism of this selection process is not detailed in the abstract.
What specific features of the examples are used to train the neural network model?
The abstract mentions that each example includes features and labels, but it does not specify the nature of these features or how they contribute to the training process.
Original Abstract Submitted
in one embodiment, a method includes receiving, by a first client system, from one or more remote servers, a current version of a neural network model including multiple model parameters, training the neural network model on multiple examples retrieved from a local data store to generate multiple updated model parameters, wherein each of the examples includes one or more features and one or more labels, calculating a user valuation associated with the first client system, wherein the user valuation represents a measure of utility of training the neural network model on the multiple examples, and sending, to one or more of the remote servers, the trained neural network model and the user valuation, wherein the user valuation is associated with a likelihood of the first client system being selected for a subsequent training of the neural network model.
