Federated Learning Clustering for Multi-Cell Model Training
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Solution Overview
Problem
Existing methods for training machine learning models across multiple cells in mobile networks are inefficient due to the need to handle data from different distributions, leading to resource-intensive and time-consuming processes.
Innovation Solution
The proposed solution combines federated learning and transfer learning to optimize the training process for machine learning models across groups of cells. This involves clustering cells based on similarity criteria, applying federated learning within clusters, and using transfer learning for cells that do not belong to any cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional centralized training methods are used to handle data from different distributions across multiple cells, then model accuracy can be maintained, but computational resources and training time increase significantly
Solution Approach 1:
The patent segments the network nodes into multiple clusters based on model parameter similarity. Each cluster is trained independently using federated learning, dividing the large-scale training problem into smaller, more manageable sub-problems. This segmentation reduces the computational burden while maintaining model accuracy through localized training groups.
2Productivity
If federated learning is applied across all network nodes without clustering, then training time is reduced, but model accuracy deteriorates due to handling diverse data distributions
Solution Approach 1:
The patent applies local quality by creating clusters of network nodes with similar model parameters and training data characteristics. Each cluster receives localized training tailored to its specific data distribution, improving model accuracy for that particular group while the overall system benefits from parallel processing across multiple clusters.
3Productivity
If network nodes are clustered based on model parameters, then training resources are optimized, but the complexity of determining clusters increases
Solution Approach 1:
The patent implements self-service by enabling network nodes to autonomously determine their cluster membership based on comparing their model parameters with other nodes. Each node can independently assess its similarity to other nodes and self-organize into appropriate clusters without requiring complex centralized coordination, thereby reducing overall system complexity.
Data Source
AI summary
An apparatus including: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive, from a plurality of network nodes, information related to at least one parameter of models of the plurality of network nodes; determine at least one cluster of the plurality of network nodes based on at least one similarity criterion and the information related to the least one parameter of models of the plurality of network nodes; and determine at least one global model for the at least one cluster using local models of network nodes that belong to the at least one cluster.