Federated Model Learning with Dual Variables for Stable Heterogeneous Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Federated learning systems face challenges in maintaining stability due to imbalanced dataset distributions and varying computational and communication capabilities among user terminals, while also facing risks of data leakage and accuracy reduction from noise addition for privacy protection.
Innovation Solution
A federated learning system with model learning apparatus connected via a network, utilizing mini-batch extraction, model parameter update, dual-variable calculation and transmission, and setting units to adjust model parameter updates with optimal coefficients, enabling stable learning across heterogeneous terminals and reducing data leakage risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If noise is added to the user-generated model in federated learning to reduce the risk of reproducing original data, then the risk of data leakage is reduced, but the accuracy of the trained model deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the noise level (variance parameter) in the model updates based on the communication environment and learning progress. The noise variance is modified as a function of communication round number and network conditions, allowing the system to optimize between privacy protection and model accuracy throughout the federated learning process
2Adaptability or versatility
If federated learning is performed with heterogeneous user terminals having different computational capabilities and network speeds, then the system can serve diverse users, but stable federated learning becomes difficult to achieve
Solution Approach 1:
The patent implements dynamics by making the learning process adaptive through dynamic adjustment of learning rates, noise levels, and communication frequencies based on real-time feedback from each user terminal's performance and network conditions. This allows the system to accommodate heterogeneous devices while maintaining overall learning stability
Solution Approach 2:
The system employs feedback mechanisms where each user terminal reports local training status and model performance metrics back to the coordination server. Based on this feedback, the server adjusts global learning parameters and communication strategies to ensure stable convergence across heterogeneous devices
3Reliability
If the noise level is increased to reduce the risk of reproducing original data, then privacy protection is improved, but the accuracy of the trained model decreases
Solution Approach 1:
The patent applies periodic action by implementing staged noise addition where different noise levels are applied at different stages of the federated learning process. Higher noise is applied during early stages for stronger privacy protection, while progressively lower noise is applied in later stages to improve model accuracy, creating an optimal balance between privacy and performance
Data Source
AI summary
A federated learning system includes a plurality of model learning apparatus. Each model learning apparatus is connected to any of the other model learning apparatus via a network. The model learning apparatus includes a mini-batch extraction unit, a model parameter update unit, a dual-variable calculation/transmission unit, a dual-variable reception unit, and a dual-variable setting unit. The model parameter update unit is configured to perform learning using a dual variable, a step size, a mini-batch of model training data, a constraint parameter, and a coefficient γ using a predetermined optimal value n and a predetermined hyperparameter α, thereby updating a model parameter. The dual-variable calculation/transmission unit is configured to calculate and transmit a dual variable using the model parameter updated by the model parameter update unit and a coefficient γ for each other model learning apparatus connected to the model learning apparatus.