Federated Learning Model Training Data Classification
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Solution Overview
Problem
Federated learning environments face challenges with catastrophic forgetting, where local models tend to forget previously learned data, leading to higher probabilities of forgetting events due to data aggregation and model updates, affecting the performance and stability of deep learning models.
Innovation Solution
The method involves classifying training data into 'forgettable' and 'unforgettable' samples based on the model's performance before and after retraining, adjusting the ratio of these samples in a mini-batch to a preset ratio, and using this adjusted mini-batch for model training, ensuring that forgettable samples are repeatedly exposed to improve learning performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional federated learning aggregation is used, then model diversity and information protection are maintained, but catastrophic forgetting occurs leading to degraded model performance
Solution Approach 1:
The training data is segmented into two distinct categories: forgettable samples and unforgettable samples. This segmentation allows the system to apply different handling strategies to different types of data, preventing catastrophic forgetting by identifying and preserving important samples while allowing less critical ones to be updated or discarded during federated learning aggregation.
Solution Approach 2:
The system changes the parameter of sample selection by adjusting the ratio of forgettable to unforgettable samples in the mini-batch according to a preset ratio. This parameter adjustment ensures that the model receives appropriate proportions of different sample types during training, balancing the need to learn new information while retaining important previously learned information.
2Productivity
If all training data is used uniformly, then training simplicity is maintained, but learning efficiency decreases due to lack of prioritization
Solution Approach 1:
The training data is divided into forgettable and unforgettable samples, enabling the system to prioritize processing of important samples. This segmentation improves learning efficiency by ensuring that critical data receives appropriate attention during training, while still maintaining a manageable training process through systematic categorization rather than random selection.
Solution Approach 2:
The system adjusts the sampling ratio parameter to optimize learning efficiency. By controlling the proportion of forgettable versus unforgettable samples in the training mini-batch, the system can improve convergence and performance without requiring completely redesigning the training pipeline, thus balancing efficiency gains with process complexity.
Data Source
AI summary
A local model training method of a federated learning framework implementing training data classification is provided. In the local model training method, a client may classify training data into two categories, generate a learning mini-batch by adjusting a ratio between samples classified into the two categories and included in the mini-batch to a preset ratio, and train a learning model using the mini-batch with the adjusted sample ratio.