17931276. FINDING SIMILAR PARTICIPANTS IN A FEDERATED LEARNING ENVIRONMENT simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 FINDING SIMILAR PARTICIPANTS IN A FEDERATED LEARNING ENVIRONMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FINDING SIMILAR PARTICIPANTS IN A FEDERATED LEARNING ENVIRONMENT - 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 Original Abstract Submitted
FINDING SIMILAR PARTICIPANTS IN A FEDERATED LEARNING ENVIRONMENT
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Soujanya Soni of BANGALORE (IN)
Sameep Mehta of BANGALORE (IN)
FINDING SIMILAR PARTICIPANTS IN A FEDERATED LEARNING ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 17931276 titled 'FINDING SIMILAR PARTICIPANTS IN A FEDERATED LEARNING ENVIRONMENT
Simplified Explanation
The abstract describes a method, computer program, and computer system for determining similar nodes in a federated learning environment. Data from a node is retrieved, frequency distributions are calculated and transmitted to an aggregator, and similarities between nodes are identified based on received frequency distributions.
- Data retrieval and calculation: Data associated with a node in a federated learning environment is retrieved, and a frequency distribution is calculated.
- Transmission to aggregator: The calculated frequency distribution is transmitted to an aggregator for processing.
- Receiving frequency distributions: Frequency distributions from other nodes in the federated learning environment are received from the aggregator.
- Similarity identification: Based on the received frequency distributions, similarities between the node and other nodes are identified.
Potential Applications
This technology can be applied in various fields such as healthcare, finance, and marketing for analyzing and comparing data across different nodes in a federated learning environment.
Problems Solved
- Efficient comparison of data across multiple nodes in a federated learning environment. - Identification of similarities between nodes without sharing raw data.
Benefits
- Enhanced privacy and security by not sharing raw data between nodes. - Improved data analysis and decision-making based on identified similarities.
Potential Commercial Applications
Enhanced Data Analysis in Federated Learning Environments
Original Abstract Submitted
A method, computer program, and computer system are provided for determining similar nodes in a federated learning environment. Data corresponding to a dataset associated with a node in the federated learning environment is retrieved by the node. A frequency distribution associated with the dataset is calculated and transmitted to an aggregator. One or more frequency distributions associated with one or more other nodes in the federated learning environment are received from the aggregator. Based on the received frequency distributions associated with the one or more other nodes, a similarity between the node and a subset of the one or more other nodes is identified.
