Federated Network Model Training with Dataset-Based Node Selection
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Solution Overview
Problem
Distributed node selection in federated learning systems is challenged by data heterogeneity and potential malicious behavior, leading to biased and poorly performing models.
Innovation Solution
A central node receives local dataset information from distributed nodes to assess dataset quality and detect potential biases or malicious behavior, assigning scores and selecting nodes for training based on these criteria, and generating aggregated model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If distributed nodes participate in federated learning without selection, then system participation and data collection are improved, but model quality and reliability deteriorate due to data heterogeneity and malicious behavior
Solution Approach 1:
The central node performs preliminary evaluation of local datasets by receiving and analyzing dataset information (size, statistics, bias metrics) from distributed nodes before selecting them for training. This preliminary action filters out malicious nodes and ensures only nodes with quality datasets participate, resolving the contradiction by preparing and vetting data sources in advance.
Solution Approach 2:
The system changes the selection parameter from simple participation to scored evaluation based on multiple criteria (dataset size, class distribution, bias metrics). By transforming the selection mechanism into a parameter-based scoring system, the system maintains broad participation while ensuring model quality through quantitative assessment.
2Productivity
If all distributed nodes are selected for training, then training efficiency and participation are improved, but model accuracy deteriorates due to biased or malicious node contributions
Solution Approach 1:
The system evaluates and selects distributed nodes based on the local quality of their specific datasets. Each node is assessed individually on metrics relevant to its local data (class distribution, bias, size), and only nodes meeting quality thresholds are selected. This local quality approach maintains training efficiency while ensuring model accuracy by excluding low-quality contributors.
Solution Approach 2:
The central node receives feedback in the form of local dataset information from distributed nodes and uses this feedback to make informed selection decisions. The feedback loop allows continuous assessment of node quality and enables the system to adjust participation based on actual dataset characteristics, balancing efficiency and accuracy.
3Quantity of substance
If nodes with large datasets are selected, then training data quantity is improved, but system complexity increases due to evaluation and selection processes
Solution Approach 1:
The evaluation process is segmented into distinct components: receiving dataset information, analyzing specific metrics (size, statistics, bias), scoring nodes, and making selection decisions. This segmentation of the complex evaluation process into manageable steps reduces operational complexity while maintaining the ability to select nodes based on data quantity and quality.
Data Source
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AI summary
The disclosure relates to an apparatus comprising: means for receiving, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node; means for determining whether to select the distributed node for training a local model for managing a network based on the local dataset information; and means for sending, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network.