Federated Learning Reliability-Based Node Selection
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Solution Overview
Problem
In distributed machine learning, unreliable user equipment (UE) nodes and low-quality training data can significantly impact the accuracy and efficiency of the training process, leading to inferior performance or slowed convergence of the machine learning model.
Innovation Solution
An apparatus and method that obtain reliability values for each UE and its training data set, selecting a subset of UEs to perform machine learning training based on these values to ensure only reliable nodes and high-quality data are used, thereby reducing the impact of unreliable nodes and low-quality data on the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all user equipments are included in the machine learning training process, then the quantity of training data increases, but the reliability of the training results deteriorates due to unreliable nodes and low-quality data
Solution Approach 1:
The system segments the group of user equipments into multiple subsets based on reliability values. Instead of treating all UEs uniformly, the federated learning server divides them into different groups (e.g., high-reliability subset, medium-reliability subset, low-reliability subset) and applies different training strategies to each segment, thereby maintaining data quantity while ensuring reliability through selective inclusion.
Solution Approach 2:
The system applies local quality by assigning different reliability weights to different user equipments based on their individual reliability values. Each UE's contribution to the training process is adjusted according to its local quality assessment, allowing high-reliability nodes to have greater influence while reducing the impact of unreliable nodes on the overall training results.
2Adaptability or versatility
If unreliable user equipment nodes are included in the training process, then the diversity of training data increases, but the accuracy of the machine learning model deteriorates
Solution Approach 1:
The system changes the reliability parameter of each user equipment based on assessed criteria (data quality, node performance, historical behavior). By dynamically adjusting the reliability parameter for each UE, the system can control the degree to which diverse but potentially unreliable data influences the model training, thereby maintaining diversity while protecting accuracy through parameter-based filtering.
Solution Approach 2:
The system implements feedback mechanisms where the federated learning server evaluates the performance and data quality of each UE during the training process. Based on this feedback, the server adjusts the reliability values and reconfigures which UEs participate in subsequent training rounds, creating a closed-loop system that maintains accuracy while preserving beneficial diversity.
3Reliability
If a subset of user equipments is selected based on reliability values, then the reliability of training results improves, but the quantity of training data decreases
Solution Approach 1:
The system segments UEs into multiple reliability-based subsets rather than selecting a single subset. This allows the system to maintain training processes with different reliability thresholds for different tasks or model types, thereby preserving more training data while still ensuring reliability for critical training operations through selective segment usage.
4Reliability
If reliability assessment is performed for each user equipment, then the reliability of training results improves, but the complexity of the system increases
Solution Approach 1:
The system implements self-service by enabling user equipments to perform self-assessment of their own reliability characteristics. Each UE evaluates its own data quality, processing capabilities, and historical performance, then reports these self-assessed reliability values to the federated learning server. This reduces the computational burden on the server and simplifies the overall system architecture while still achieving reliable training result selection.
Data Source
AI summary
According to an example aspect of the present invention, there is provided an apparatus configured to obtain reliability values for each user equipment in a group of user equipments, obtain, for each user equipment in the group, a reliability value for a training data set stored in the user equipment, each user equipment storing a distinct training data set, and direct a subset of the group of user equipments to separately perform a machine learning training process in the user equipments in the subset, wherein the apparatus is configured to select the subset based on the reliability values for the user equipments and the reliability values for the training data sets.