Federated Learning Model Layer Tuning for Catastrophic Forgetting
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Solution Overview
Problem
Deep learning models face challenges in continuous learning and adaptation with new data due to catastrophic forgetting, especially in scenarios with limited data and privacy concerns that prevent sharing of training data, leading to inefficiencies and accuracy loss.
Innovation Solution
A continuous federated learning system that selectively tunes specific layers of the local model based on incremental data, using a global model and model tuning modules to generate a retrained model without retraining the entire network, thus maintaining performance on previous tasks while adapting to new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the entire deep learning model is retrained with incremental new data, then the model adapts to new data, but catastrophic forgetting occurs and previously learned knowledge is lost
Solution Approach 1:
The patent segments the deep learning model into multiple independent layers and selectively updates only those layers that are relevant to the new incremental data. This segmentation allows the model to adapt to new data while preserving previously learned knowledge in unchanged layers, thereby resolving catastrophic forgetting.
Solution Approach 2:
The patent applies local quality by making different parts of the model have different update behaviors. Some layers are updated with new data while other layers retain their original weights, creating a heterogeneous update strategy that adapts to local data characteristics without globally overwriting learned knowledge.
2Adaptability or versatility
If the entire deep learning model is retrained with incremental new data, then the model adapts to new data, but significant computational resources and time are consumed
Solution Approach 1:
By segmenting the model into updateable and non-updateable layers, the patent reduces the computational scope from the entire model to only relevant portions. This segmentation dramatically decreases retraining time and computational resource requirements while maintaining adaptation capability.
Solution Approach 2:
The patent applies partial action by updating only the necessary subset of model layers rather than the entire model. This partial update approach achieves sufficient adaptation to new data with significantly reduced computational effort and time compared to full model retraining.
3Adaptability or versatility
If the entire deep learning model is retrained with incremental new data, then the model adapts to new data, but data privacy concerns are violated due to required data sharing
Solution Approach 1:
The patent enables distributed selective layer updates where each local site independently updates its own model layers using local data without sharing data with other sites. This segmentation of the training process across distributed systems achieves adaptation while preserving data privacy.
Solution Approach 2:
Each local site performs self-service by independently updating its model layers using only its own local data. This eliminates the need for data sharing or centralized data collection, thereby adapting to new data while maintaining data privacy and security.
4Productivity
If selective layers are tuned instead of the entire model, then computational efficiency improves and adaptation speed increases, but model complexity increases due to layer selection mechanisms
Solution Approach 1:
The patent applies preliminary action by pre-identifying and marking specific layers as updateable before the incremental learning process begins. This preliminary configuration simplifies the subsequent selective update process, reducing the complexity of real-time layer selection while maintaining fast adaptation speed.
Data Source
AI summary
A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.