Federated GAN Training via Local Feature Extraction
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Solution Overview
Problem
The challenge of training federated Generative Adversarial Networks (GANs) using private data is hindered by privacy concerns and legal obligations, restricting the ability to share data across organizational boundaries, which limits the quality and type of machine learning models that can be developed.
Innovation Solution
A computer-implemented method and system for training federated GANs using private data, where each participant system has a local feature extractor and discriminator, communicating with an aggregator system. The method involves receiving features from local feature extractors and discriminator updates from participant systems to update the aggregator's discriminator, ensuring privacy is maintained by only using extracted features for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data is transferred across organizational boundaries for training AI models, then model quality and training effectiveness are improved, but privacy concerns and legal compliance issues worsen
Solution Approach 1:
The system segments the data processing task by extracting features locally at each participant system before transmission to the aggregator. This segmentation allows the heavy computational workload to be distributed across multiple systems while only transmitting the extracted feature representations, thereby maintaining privacy while enabling model training.
Solution Approach 2:
The feature extractor acts as an intermediary component that sits between the private data and the central aggregator. It processes data locally and converts it into feature representations that can be transmitted without exposing the underlying private data, thus mediating between privacy requirements and training needs.
2Productivity
If data is shared across multiple participant systems for federated learning, then model performance is improved, but data security and privacy protection requirements worsen
Solution Approach 1:
The system extracts only the necessary feature representations from the private data at each participant system, separating the useful information needed for training from the sensitive original data. This extraction allows model performance to improve through data aggregation while the original private data remains secure and inaccessible to the aggregator and other systems.
Solution Approach 2:
Instead of transmitting the original private data, the system creates and transmits copies of the extracted feature representations. These copies contain the necessary information for model training but do not expose the underlying private data, thereby maintaining data security while enabling performance improvement.
3Adaptability or versatility
If features are extracted and transmitted to the aggregator for discriminator training, then training capability is improved, but system complexity and communication overhead worsen
Solution Approach 1:
The system performs partial action by extracting only the relevant feature representations needed for discriminator training rather than transmitting all possible data. This partial extraction reduces communication overhead and system complexity while maintaining sufficient training capability through the extracted features.
Data Source
AI summary
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.