To extend the proposed DL framework for FLSs to handle online or incremental learning scenarios, several adjustments and additions can be made.
Online Learning Mechanism: Implement an online learning mechanism that allows the model to update its parameters continuously as new data streams in. This can involve updating the model with each new data point or in small batches to adapt to changing patterns in the data.
Replay Buffer: Introduce a replay buffer to store past data samples and experiences, enabling the model to revisit and learn from previous data points. This can help in retaining knowledge from older data while incorporating new information.
Regularization Techniques: Incorporate regularization techniques like Elastic Net or L1/L2 regularization to prevent overfitting and adapt the model to new data without forgetting previous knowledge.
Dynamic Model Architecture: Design a dynamic model architecture that can adjust its complexity based on the incoming data complexity. This flexibility allows the model to adapt to varying data distributions and patterns.
Continual Learning Strategies: Implement continual learning strategies such as Elastic Weight Consolidation (EWC) or Progressive Neural Networks (PNN) to prevent catastrophic forgetting and retain knowledge from previous tasks while learning new ones.
By integrating these elements into the DL framework for FLSs, the model can effectively handle online or incremental learning scenarios, ensuring adaptability and continuous improvement over time.