Samsung electronics co., ltd. (20240135194). METHOD AND SYSTEM FOR FEDERATED LEARNING simplified abstract
Contents
- 1 METHOD AND SYSTEM FOR FEDERATED LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND SYSTEM 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
METHOD AND SYSTEM FOR FEDERATED LEARNING
Organization Name
Inventor(s)
Timothy Hospedales of Staines (GB)
METHOD AND SYSTEM FOR FEDERATED LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135194 titled 'METHOD AND SYSTEM FOR FEDERATED LEARNING
Simplified Explanation
The present techniques provide a method for training a machine learning model to update global and local versions of a model using a novel hierarchical Bayesian approach to federated learning. The models describe the generative process of clients' local data via hierarchical Bayesian modeling, allowing for distributed algorithm separability over clients without revealing their private data.
- Explanation of the patent/innovation:
- Hierarchical Bayesian approach to federated learning - Models describe generative process of clients' local data - Variational inference leads to optimization problem - Block-coordinate descent solution becomes a distributed algorithm - Clients do not reveal their private data
Potential Applications
This technology can be applied in various fields such as healthcare, finance, and telecommunications for collaborative machine learning tasks where data privacy is a concern.
Problems Solved
- Protecting privacy of individual client data in federated learning - Updating global and local versions of a model without compromising data security
Benefits
- Improved model accuracy through collaborative learning - Enhanced data privacy protection for clients - Efficient distributed algorithm for updating models
Potential Commercial Applications
- Secure collaborative machine learning platforms for industries with sensitive data - Data analysis tools for organizations with distributed data sources
Possible Prior Art
One potential prior art could be the use of differential privacy techniques in federated learning to protect individual data privacy.
Unanswered Questions
How does this hierarchical Bayesian approach compare to other methods in terms of model accuracy and computational efficiency?
The article does not provide a comparison with other methods in terms of model accuracy and computational efficiency. Further research or experimentation may be needed to evaluate the performance of this approach against existing methods.
What are the potential limitations or challenges in implementing this distributed algorithm in real-world scenarios?
The article does not discuss potential limitations or challenges in implementing the distributed algorithm in real-world scenarios. Practical considerations such as scalability, communication overhead, and compatibility with existing systems may need to be addressed before widespread adoption.
Original Abstract Submitted
broadly speaking, embodiments of the present techniques provide a method for training a machine learning, ml, model to update global and local versions of a model. we propose a novel hierarchical bayesian approach to federated learning (fl), where our models reasonably describe the generative process of clients' local data via hierarchical bayesian modeling: constituting random variables of local models for clients that are governed by a higher-level global variate. interestingly, the variational inference in our bayesian model leads to an optimisation problem whose block-coordinate descent solution becomes a distributed algorithm that is separable over clients and allows them not to reveal their own private data at all, thus fully compatible with fl.
