To adapt the FedStitch framework for incremental learning of new tasks, several strategies can be implemented to allow for the seamless integration of new information without the need for retraining the entire stitched network. One approach is to maintain a dynamic block pool that includes not only the original pre-trained blocks but also newly trained blocks that correspond to the incremental tasks. This would enable clients to select and stitch together relevant blocks from both the existing and new models based on the specific requirements of the new task.
Additionally, the framework can incorporate a mechanism for continual learning, where the model retains knowledge from previously learned tasks while adapting to new ones. This can be achieved through techniques such as elastic weight consolidation (EWC) or progressive neural networks, which help mitigate catastrophic forgetting by preserving important weights associated with earlier tasks.
Moreover, the RL-based weighted aggregation can be fine-tuned to account for the evolving nature of the data distributions as new tasks are introduced. By continuously updating the weights assigned to clients based on their performance on both old and new tasks, the FedStitch framework can ensure that the aggregation process remains robust and effective.
Finally, implementing a feedback loop that allows clients to provide performance metrics on the newly stitched networks can facilitate real-time adjustments to the block selection process, ensuring that the framework remains responsive to the changing landscape of tasks and data distributions. This adaptability will enhance the overall efficiency and effectiveness of the FedStitch framework in supporting incremental learning scenarios.