18512195. METHOD AND SYSTEM FOR FEDERATED LEARNING simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
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 How does the hierarchical Bayesian approach compare to other federated learning methods in terms of model accuracy and privacy protection?
- 1.11 What are the potential limitations or challenges of implementing this hierarchical Bayesian approach in real-world applications?
- 1.12 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 18512195 titled 'METHOD AND SYSTEM FOR FEDERATED LEARNING
Simplified Explanation
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 optimization 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.
- Hierarchical Bayesian approach to Federated Learning
- Models describe generative process of clients' local data
- Variational inference leads to optimization problem
- Block-coordinate descent solution as a distributed algorithm
- Clients do not reveal their private data
Potential Applications
The technology can be applied in various fields such as healthcare, finance, and telecommunications for collaborative machine learning tasks without compromising data privacy.
Problems Solved
1. Privacy concerns in collaborative machine learning 2. Efficient model updating without sharing sensitive data
Benefits
1. Enhanced data privacy protection 2. Improved model accuracy through collaborative learning 3. Scalability in distributed machine learning tasks
Potential Commercial Applications
"Privacy-Preserving Collaborative Machine Learning in Healthcare"
Possible Prior Art
There are existing methods for federated learning and privacy-preserving machine learning techniques, but the specific hierarchical Bayesian approach described in this patent application appears to be novel.
Unanswered Questions
How does the hierarchical Bayesian approach compare to other federated learning methods in terms of model accuracy and privacy protection?
The article does not provide a direct comparison with other federated learning methods in terms of model accuracy and privacy protection. Further research or experimentation may be needed to evaluate the performance of this approach against existing methods.
What are the potential limitations or challenges of implementing this hierarchical Bayesian approach in real-world applications?
The article does not discuss potential limitations or challenges of implementing this approach in real-world applications. Factors such as computational complexity, communication overhead, and scalability issues may need to be considered for practical deployment.
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.
