Continuous Federated Learning With Synthetic Data Against Forgetting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep learning models face challenges in continuous learning and adapting with new data due to catastrophic forgetting, especially in scenarios where data privacy concerns prevent data sharing, leading to significant loss of accuracy.
Innovation Solution
A continuous federated learning framework is established using a global model and local models, where retraining occurs through synthetic datasets generated from actual datasets, rather than sharing actual datasets, to mitigate catastrophic forgetting and maintain data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If retraining is performed with incremental new data, then the model adapts to new information, but previously learned knowledge is lost (catastrophic forgetting)
Solution Approach 1:
The system performs preliminary actions by creating synthetic data representations and storing them in a data lake before actual retraining occurs. This preliminary preparation allows the model to access previously learned knowledge patterns during incremental retraining, mitigating catastrophic forgetting while adapting to new data distributions
Solution Approach 2:
Instead of directly using original training data for retraining, the system creates synthetic copies of the data through generative models. These synthetic representations preserve the essential patterns and knowledge from original data while allowing flexible retraining, thus maintaining model accuracy during adaptation to new incremental data
2Reliability
If actual datasets are shared between global and local sites for retraining, then model accuracy is maintained, but data privacy concerns are violated
Solution Approach 1:
The system introduces synthetic data as an intermediary between original datasets at local sites and the global model retraining process. This intermediary allows knowledge transfer and model improvement without exposing sensitive actual data, thus maintaining model accuracy while protecting data privacy through the mediating synthetic representations
Solution Approach 2:
The system creates synthetic copies of actual datasets that preserve statistical properties and learning patterns while removing sensitive information. These synthetic copies are then shared and used for retraining, enabling model accuracy maintenance without violating data privacy since the copies do not contain actual sensitive data
3Reliability
If retraining from scratch is performed to address catastrophic forgetting, then previously learned knowledge is preserved, but significant time and computational resources are lost
Solution Approach 1:
Instead of performing complete retraining from scratch, the system applies partial retraining using synthetic data representations. This partial action is sufficient to preserve previously learned knowledge patterns while adapting to new data, significantly reducing retraining time and computational resources compared to full retraining
Solution Approach 2:
The system performs preliminary processing to create compact synthetic data representations that capture essential knowledge patterns. This preliminary action enables faster subsequent retraining iterations, reducing the time and computational resources needed while maintaining model accuracy
Data Source
AI summary
A computer implemented method is provided. The method includes establishing, via multiple processors, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites. The method also includes retraining or retuning, via the multiple processors, the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets.