Base Station Scheduling for Federated Learning Dataset Distribution
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Solution Overview
Problem
In wireless communication systems, the lack of data sharing among institutions and varying user requirements lead to reduced model accuracy and inefficiencies in machine learning, particularly due to data privacy concerns and the need for personalized models, which existing technologies fail to address effectively.
Innovation Solution
A data processing method and apparatus that schedules user equipment (UE) for federated learning based on the distribution characteristics of their local datasets, allowing for direct participation without reporting data to the core network or data center, thereby improving training efficiency and model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is shared among institutions for machine learning training, then model accuracy is improved, but data privacy and security are compromised
Solution Approach 1:
The patent introduces a base station as an intermediary that coordinates federated learning among multiple user equipments. The base station aggregates model parameters from different UEs without directly accessing their local datasets, enabling collaborative model training while maintaining data privacy through the intermediary's coordination role
Solution Approach 2:
The patent uses model parameter copies instead of original data for training. Each UE trains local models using its own data and sends only parameter updates (copies) to the base station, which aggregates these copies to update the global model, eliminating the need to share actual data while still improving model accuracy
2Device complexity
If a unified machine learning model is used for all users, then system complexity is reduced, but model accuracy for specific user requirements deteriorates
Solution Approach 1:
The patent implements local model customization where each UE maintains its own local model parameters that are tailored to its specific data characteristics and requirements. The base station coordinates these local models with the global model, allowing each user to have customized model quality while maintaining overall system coordination
Solution Approach 2:
The patent segments the machine learning system into multiple independent local models at each UE and a coordinated global model at the base station. This segmentation allows different parts of the system to serve different purposes - local models handle user-specific requirements while the global model provides overall coordination, resolving the contradiction between complexity and accuracy
3Measurement precision
If all user equipments participate in federated learning, then model accuracy is improved, but training time and resource consumption increase
Solution Approach 1:
The patent applies partial action by selecting only certain UEs to participate in each federated learning round based on their data distribution characteristics and current training needs. The base station identifies and schedules specific UEs rather than requiring all UEs to participate in every training iteration, reducing overall training time while maintaining model accuracy through selective participation
Data Source
AI summary
A data processing method includes determining, by a base station, a distribution characteristic of a local dataset of at least one user equipment (UE), and scheduling, by the base station, based on the distribution characteristic of the local dataset, a target UE from the at least one UE for participating in federated learning.