Non-Iterative Federated Learning via Synthetic Data Generation
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Solution Overview
Problem
Federated learning techniques face challenges such as the need for iterative updates, synchronization of model updates, and restrictive assumptions about model architecture, which can lead to slow convergence and error accumulation, especially when dealing with decentralized data and varying participation rates from agents.
Innovation Solution
A non-iterative approach where local models generate synthetic datasets that are used to train a global model, allowing for flexible model architecture and asynchronous updates without synchronization, using generator models to recreate original datasets and classify them, enabling continuous model refinement and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative updates are used in federated learning, then model convergence can be achieved, but the training process becomes slow and requires synchronization of model updates
Solution Approach 1:
The patent applies preliminary action by having each local model generate synthetic training data before the global model training phase. This pre-generated synthetic data eliminates the need for iterative updates during global model training, as the global model can be trained directly on the collected synthetic datasets from all local models, thereby achieving convergence without slow iterative synchronization.
2Stability of the object's composition
If synchronization of model updates is implemented, then consistent global model can be achieved, but flexibility is reduced when agents withdraw or join the federated learning process
Solution Approach 1:
The patent uses copying by having each local model generate synthetic datasets that replicate the characteristics of the original local training data. These synthetic copies can be independently created and submitted without requiring synchronization with other agents, allowing agents to join or withdraw flexibly while maintaining global model consistency through the collected synthetic data.
3Reliability
If traditional federated learning is used, then data privacy is maintained, but model architecture flexibility is restricted
Solution Approach 1:
The patent extracts the training data generation function from the centralized training process and places it at each local model. By having local models generate synthetic datasets locally and submit only these synthetic datasets (not raw data) to the global model training, the system maintains data privacy while allowing each local model to use its own architecture to generate data, thus enabling model architecture flexibility.
Data Source
AI summary
Techniques for non-iterative federated learning include receiving local models from agents, generating synthetic datasets for the local models, and producing outputs using the local models and the synthetic datasets. A global model is trained based on the synthetic datasets and the outputs.