Local Neural Network Model Evaluation for Federated Learning Stability
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Solution Overview
Problem
Federated learning techniques for neural network models face issues with stability and accuracy due to failures and attacks during global model transmission, leading to compromised accuracy of the global neural network model and reduced evaluation accuracy.
Innovation Solution
An electronic apparatus is configured to receive and evaluate a global neural network model, compare accuracy levels before and after training, and determine whether to transmit updated model information to a server, while ensuring secure communication and data protection by using SSL/TLS encoding and managing data sets locally to prevent data corruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train global neural network model across multiple devices, then data privacy is improved and network costs are reduced, but model accuracy and stability deteriorate due to failures and attacks during global model transmission
Solution Approach 1:
The patent introduces local neural network models as intermediaries that receive the global model, train locally using local datasets, and generate updated global models. This intermediary training process protects the global model from direct exposure to transmission failures and attacks, thereby maintaining model accuracy and stability while still enabling federated learning across multiple devices.
Solution Approach 2:
The patent performs preliminary evaluation of the global model's accuracy before transmission and after local training. By evaluating the model in advance and comparing accuracy metrics, the system can identify potential issues before they affect the overall federated learning process, preventing propagation of corrupted models and maintaining reliability.
2Manufacturing precision
If all data from tens of thousands of local electronic apparatuses is transmitted to a central server, then comprehensive model training is achieved, but network traffic and storage costs increase significantly
Solution Approach 1:
The patent segments the federated learning process into local training phases and global aggregation phases. Each local device trains its own neural network model using local data, and only the trained model parameters are transmitted to the server for aggregation. This segmentation eliminates the need to transmit raw data, dramatically reducing network traffic and storage costs while maintaining comprehensive model training quality through diverse local datasets.
3Measurement precision
If global neural network model is transmitted frequently for updates, then model accuracy is improved, but stability deteriorates due to increased exposure to failures and attacks
Solution Approach 1:
The patent implements a feedback mechanism where the accuracy of the global model is evaluated both before transmission and after local training. The system compares accuracy metrics and uses this feedback to determine whether to proceed with model updates. This feedback loop ensures that only accurate and stable models are transmitted and applied, preventing propagation of corrupted models while still achieving continuous accuracy improvement.
Data Source
AI summary
An electronic apparatus, including: a communication interface; a memory configured to store at least one instruction; and at least one processor configured to: receive information regarding a global neural network model and information regarding evaluation data from a server using the communication interface; obtain a data set for training the global neural network model; train the global neural network model based on the data set; evaluate the trained global neural network model by inputting the evaluation data to the trained global neural network model; and determine whether to transmit information regarding the trained global neural network model to the server based on a result of the evaluating.