18063813. TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK simplified abstract (International Business Machines Corporation)
Contents
- 1 TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Privacy-Preserving Federated GAN Training
- 1.13 Original Abstract Submitted
TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK
Organization Name
International Business Machines Corporation
Inventor(s)
Killian Levacher of Dublin (IE)
TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18063813 titled 'TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK
Simplified Explanation
The patent application describes a method, computer system, and computer program product for training a federated generative adversarial network (GAN) using private data. This involves communication between an aggregator system and multiple participant systems, each with their own local feature extractor and discriminator.
- The method involves receiving features extracted from private data at the participant systems and using them as input for the discriminator at the aggregator system.
- Updates to the discriminator at the aggregator system are received from the local discriminators at the participant systems, which are trained locally.
Key Features and Innovation
- Training a federated GAN using private data from multiple participant systems.
- Communication between an aggregator system and participant systems with local feature extractors and discriminators.
- Updating the discriminator at the aggregator system based on parameter updates from local discriminators.
Potential Applications
This technology could be applied in industries where privacy of data is crucial, such as healthcare, finance, and telecommunications. It could also be used in research settings where collaboration on sensitive data is necessary.
Problems Solved
This technology addresses the challenge of training GANs using private data from multiple sources while maintaining data privacy and security. It enables collaborative training without sharing sensitive information.
Benefits
- Enhanced privacy and security in training GANs with private data.
- Efficient collaboration and training across multiple participant systems.
- Improved accuracy and performance of GAN models trained on sensitive data.
Commercial Applications
Title: Privacy-Preserving Federated GAN Training for Secure Collaborative AI This technology could be utilized in industries such as healthcare for collaborative research on patient data, in finance for fraud detection models trained on sensitive financial information, and in telecommunications for improving network security through collaborative GAN training.
Prior Art
There may be prior research on federated learning and GAN training methods that involve privacy-preserving techniques. Researchers in the fields of machine learning, privacy-preserving data analysis, and collaborative AI may have explored similar concepts.
Frequently Updated Research
Researchers in the fields of federated learning, privacy-preserving machine learning, and collaborative AI are continuously exploring new methods and techniques for secure and efficient collaborative training of AI models using private data.
Questions about Privacy-Preserving Federated GAN Training
How does this technology ensure data privacy and security when training GANs across multiple participant systems?
The technology ensures data privacy and security by allowing each participant system to keep their private data local and only share extracted features with the aggregator system for model training. This minimizes the risk of exposing sensitive information during the collaborative training process.
What are the potential challenges in implementing this technology in real-world applications?
Implementing this technology in real-world applications may face challenges related to data compatibility, communication protocols between participant systems and the aggregator, and ensuring the confidentiality of data during the training process. Collaborating with different organizations with varying data privacy policies could also pose challenges in deployment.
Original Abstract Submitted
A method, computer system, and computer program product are provided for training a federated generative adversarial network (GAN) using private data. The method is carried out at an aggregator system having a generator and a discriminator, wherein the aggregator system is in communication with multiple participant systems each having a local feature extractor and a local discriminator. The method includes: receiving, from a feature extractor at a participant system, a set of features for input to the discriminator at the aggregator system, wherein the features include features extracted from private data that is private to the participant system; and receiving, from one or more local discriminators of the participant systems, discriminator parameter updates to update the discriminator at the aggregator system, wherein the local discriminators are trained at the participant systems.
