International business machines corporation (20240193428). TRAINING A FEDERATED GENERATIVE ADVERSARIAL NETWORK simplified abstract
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 Secure Federated GAN Training for Sensitive Data Industries
- 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 20240193428 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 a participant system and inputting them to the discriminator at the aggregator system.
- Updates to the discriminator at the aggregator system are received from 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 with 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 between multiple parties is necessary.
Problems Solved
This technology addresses the challenge of training GANs using private data from different sources while maintaining data privacy and security. It enables collaboration and training of AI models without sharing sensitive information.
Benefits
- Enhanced privacy and security of data.
- Efficient training of AI models using distributed private data.
- Facilitates collaboration and knowledge sharing without compromising data confidentiality.
Commercial Applications
Title: Secure Federated GAN Training for Sensitive Data Industries This technology could be utilized in healthcare for medical image generation, in finance for fraud detection, and in telecommunications for signal processing. It could also be valuable in research institutions for collaborative AI model training.
Prior Art
There may be prior research on federated learning and GAN training with private data. Researchers can explore academic journals, conference papers, and patent databases for related work in this field.
Frequently Updated Research
Researchers in the field of federated learning and privacy-preserving machine learning are continuously exploring new methods and techniques for secure and efficient model training using distributed private data.
Questions about Secure Federated GAN Training for Sensitive Data Industries
1. How does this technology ensure the privacy and security of data during training? 2. What are the potential challenges in implementing this technology in real-world applications?
1. How does this technology ensure the privacy and security of data during training? This technology ensures privacy and security by allowing each participant system to keep their private data local and only share extracted features with the aggregator system. The updates to the discriminator are also securely transmitted between the local discriminators and the aggregator system.
2. What are the potential challenges in implementing this technology in real-world applications? Some potential challenges in implementing this technology in real-world applications include ensuring compatibility between different participant systems, managing communication and data transfer securely, and addressing regulatory compliance issues related to data privacy and security.
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.
