Federated Learning Extractor State Information Aggregation
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Solution Overview
Problem
Current AI model training methods require significant computational resources and centralize user data, leading to privacy risks and high network costs, which are addressed by federated learning but can be improved in terms of representation and performance.
Innovation Solution
A federated learning system with a central server and client devices, where the central server transmits state information of its learning model to client devices, which train and update the model using individual data, and then upload the updated state information back, utilizing a knowledge distillation technique to improve model representation and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud computing is used to train AI models centrally, then computational efficiency is improved, but user data privacy is compromised and network costs increase
Solution Approach 1:
The system segments the AI model training process into local extraction modules at each client device and a central aggregation module at the server. Each client device independently extracts features from its local data without transmitting raw data, thereby maintaining privacy while enabling centralized model training through aggregated feature information.
Solution Approach 2:
The patent introduces an intermediary feature extraction mechanism where client devices extract and transmit only essential feature information rather than raw data. This intermediary representation serves as a mediator between local data processing and central model training, reducing network transmission costs and privacy risks while maintaining training effectiveness.
2Measurement precision
If all user data is transmitted to the cloud for training, then model training accuracy is improved, but network costs and privacy risks significantly increase
Solution Approach 1:
The system extracts only the essential feature information from raw user data at the client device level using local extraction modules. This extracted feature information is then transmitted to the cloud for model training, thereby maintaining training accuracy while significantly reducing network transmission costs and data privacy risks.
Solution Approach 2:
The patent implements local feature extraction at each client device, allowing each device to process its data locally and transmit only the extracted features. This local processing approach maintains the quality and accuracy of training data while minimizing network transmission requirements and associated costs.
3Object-affected harmful factors
If federated learning is used to avoid data centralization, then privacy protection is improved, but model representation and performance are degraded
Solution Approach 1:
The system merges the advantages of federated learning with centralized feature aggregation. Client devices perform local feature extraction and training independently (preserving privacy), while the central server aggregates these extracted features to build and update the global model, thereby improving overall model representation beyond what pure federated learning achieves.
Solution Approach 2:
The patent transitions from traditional federated learning by adding a feature extraction dimension. Instead of directly aggregating model parameters or raw data, the system extracts features as an intermediate representation, creating a new dimensional space for information aggregation that improves model representation while maintaining privacy protection.
Data Source
AI summary
A federated learning system includes: a central server including a central learning model containing an extractor; and client devices each including a local learning model performing federated learning with the central learning model. The local learning model includes an extractor and a classifier, and the central server transmits state information of the extractor in the central learning model to a client device among the client devices, receives state information of the extractor in the local learning model from the client device, and updates the extractor in the central learning model using the received state information, and the client device uploads the state information to the extractor in the local learning model, trains the extractor and the classifier in the local learning model using individual training data, and transmits state information of the extractor trained in the local learning model to the central server.