Digital Twin Guided Federated Learning for Faster Model Convergence
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Solution Overview
Problem
Existing federated learning methods face inefficiencies due to long training times when dealing with numerous data providers and large data volumes, hindering the rapid acquisition of a trained global model.
Innovation Solution
Implementing a digital twin platform in a first data provider to simulate the running process of an entity target device, generating a digital twin model that reflects the relationship between target results and device states, and using this model to guide second data providers in training, thereby improving training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional federated learning is used with many data providers and large data volumes, then comprehensive model training is achieved, but training time becomes excessively long
Solution Approach 1:
The digital twin platform performs preliminary simulation and testing of device running processes before actual federated learning training. By pre-simulating various device states and outcomes in the digital twin environment, the system prepares optimized training data and scenarios in advance, significantly reducing the time required for actual model training across multiple data providers while ensuring comprehensive coverage of device states
Solution Approach 2:
The patent creates a digital twin model that replicates the running process of entity target devices. This virtual copy allows the system to simulate and test device behaviors, outcomes, and training scenarios without requiring actual physical device operation. The digital twin model serves as a surrogate that accelerates the training process by performing computations and simulations in a virtual environment before deploying to real devices
2Measurement precision
If digital twin simulation is performed for all data providers, then model training accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides data providers into two distinct groups: a first data provider that operates and maintains the digital twin platform, and second data providers that participate in federated learning using the digital twin model. This segmentation allows the complex digital twin simulation functionality to be concentrated in one entity, while other participants benefit from its outputs without bearing the full system complexity burden
Solution Approach 2:
The digital twin model acts as an intermediary between the first data provider's simulation capabilities and the second data providers' training processes. Instead of requiring all data providers to implement full simulation capabilities, the digital twin model serves as a shared intermediate artifact that translates complex device running processes into usable training data and scenarios for federated learning participants
Data Source
AI summary
An information processing system, method, apparatus, device and a storage medium, where the system includes: a first data provider, a collaborator and second data providers, which participate in federated learning; the first data provider is used to generate and send a digital twin model to the collaborator; the digital twin model is used to reflect a relationship between a target result and a plurality of device running states that affect the target result; the collaborator is used to send the digital twin model to the second data providers, so that the second data providers train the digital twin model based on local data and receive corresponding model parameters; and aggregate the model parameters to obtain a global model, so that the second data providers can train on the basis of the digital twin model.