18474934. Personalized Federated Learning Via Sharable Basis Models simplified abstract (GOOGLE LLC)
Contents
- 1 Personalized Federated Learning Via Sharable Basis Models
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Personalized Federated Learning Via Sharable Basis Models - 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
Personalized Federated Learning Via Sharable Basis Models
Organization Name
Inventor(s)
Hong-You Chen of Hilliard OH (US)
Boqing Gong of Bellevue WA (US)
Mingda Zhang of Pittsburgh PA (US)
Hang Qi of Mountain View CA (US)
Personalized Federated Learning Via Sharable Basis Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 18474934 titled 'Personalized Federated Learning Via Sharable Basis Models
Simplified Explanation
The embodiments provide personalized federated learning (PFL) models through sharable federated basis models. A model architecture and learning algorithm for PFL models are disclosed. The embodiments learn a set of basis models that can be combined layer by layer to create a personalized model for each client using specifically learned combination coefficients. The set of basis models is shared with each client in a set of clients, allowing each client to generate a unique PFL based on their specific combination coefficients encoded in personalized vectors.
- Personalized federated learning (PFL) models are created using sharable federated basis models.
- A set of basis models is learned and shared with each client in a set of clients.
- Each client generates a unique PFL model based on their specifically learned combination coefficients.
- The combination coefficients are encoded in personalized vectors for each client.
Potential Applications
The technology could be applied in personalized recommendation systems, healthcare analytics, and financial forecasting.
Problems Solved
This technology solves the problem of creating personalized machine learning models for individual clients in a federated learning setting.
Benefits
The benefits of this technology include improved model personalization, enhanced privacy protection, and efficient sharing of basis models among clients.
Potential Commercial Applications
Potential commercial applications include personalized advertising, personalized healthcare diagnostics, and personalized financial advisory services.
Possible Prior Art
One possible prior art could be the use of collaborative filtering techniques in recommendation systems to personalize user experiences.
Unanswered Questions
How does this technology handle data privacy concerns?
The technology mentions enhanced privacy protection, but the specific methods or mechanisms used are not detailed in the abstract.
What computational resources are required to implement this technology?
The abstract does not provide information on the computational resources needed to train and deploy the personalized federated learning models.
Original Abstract Submitted
The embodiments are directed towards providing personalized federated learning (PFL) models via sharable federated basis models. A model architecture and learning algorithm for PFL models is disclosed. The embodiments learn a set of basis models, which can be combined layer by layer to form a personalized model for each client using specifically learned combination coefficients. The set of basis models are shared with each client of a set of the clients. Thus, the set of basis models is common to each client of the set of clients. However, each client may generate a unique PFL based on their specifically learned combination coefficients. The unique combination of coefficients for each client may be encoded in a separate personalized vector for each of the clients.
