17943839. Bilevel Optimization Based Decentralized Framework for Personalized Client Learning simplified abstract (International Business Machines Corporation)
Contents
- 1 Bilevel Optimization Based Decentralized Framework for Personalized Client Learning
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Bilevel Optimization Based Decentralized Framework for Personalized Client 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 Unanswered Questions
- 1.11 Original Abstract Submitted
Bilevel Optimization Based Decentralized Framework for Personalized Client Learning
Organization Name
International Business Machines Corporation
Inventor(s)
Songtao Lu of White Plains NY (US)
Xiaodong Cui of Chappaqua NY (US)
Mark S. Squillante of Greenwich CT (US)
Brian E.D. Kingsbury of Cortlandt Manor NY (US)
Lior Horesh of North Salem NY (US)
Bilevel Optimization Based Decentralized Framework for Personalized Client Learning - A simplified explanation of the abstract
This abstract first appeared for US patent application 17943839 titled 'Bilevel Optimization Based Decentralized Framework for Personalized Client Learning
Simplified Explanation
The decentralized bilevel optimization techniques for personalized learning over a heterogeneous network involve a distributed machine learning network with multiple nodes and datasets, as well as a bilevel learning structure at each node for optimizing features from each dataset using a decentralized bilevel optimization solver.
- Distributed machine learning network with multiple nodes and datasets
- Bilevel learning structure at each node for optimizing features from datasets
- Decentralized bilevel optimization solver used
- Maintaining distinct features from each dataset
Potential Applications
The technology can be applied in personalized learning systems, recommendation systems, and collaborative filtering algorithms.
Problems Solved
1. Efficient optimization of features from multiple datasets in a decentralized manner. 2. Personalized learning over a heterogeneous network.
Benefits
1. Improved accuracy and efficiency in personalized learning. 2. Enhanced collaboration and data sharing among nodes. 3. Scalability and adaptability to different network configurations.
Potential Commercial Applications
Optimizing personalized content delivery in e-learning platforms.
Possible Prior Art
One possible prior art could be the use of federated learning techniques in distributed machine learning networks.
Unanswered Questions
1. How does the decentralized bilevel optimization solver handle conflicts between optimizing features from different datasets? 2. What are the computational requirements of implementing this decentralized learning system on a large scale network?
Original Abstract Submitted
Decentralized bilevel optimization techniques for personalized learning over a heterogenous network are provided. In one aspect, a decentralized learning system includes: a distributed machine learning network with multiple nodes, and datasets associated with the nodes; and a bilevel learning structure at each of the nodes for optimizing one or more features from each of the datasets using a decentralized bilevel optimization solver, while maintaining distinct features from each of the datasets. A method for decentralized learning is also provided.
