Cluster-Based Host Selection in Asynchronous Federated Learning
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Solution Overview
Problem
Current federated learning systems in 5G wireless networks face challenges in efficiently collecting model updates from distributed hosts, leading to communication overhead and potential degradation in aggregated model accuracy due to ineffective selection of user equipment (UEs) for model updates, especially when data from UEs is highly correlated.
Innovation Solution
Implement a method that clusters UEs based on the correlation of their local model parameters, allowing for asynchronous model collection from a subset of UEs with mature models, and periodically re-evaluates clustering to ensure data correlation, combining synchronous and asynchronous model collection techniques to reduce communication overhead while maintaining convergence speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all distributed UEs transmit their local model parameters in each federated learning round, then the aggregated model accuracy is improved, but the communication overhead increases significantly
Solution Approach 1:
The system segments the distributed UEs into multiple clusters based on data correlation metrics. Instead of treating all UEs uniformly, each cluster is independently managed with a selected training host, reducing the number of UEs that need to communicate in each round while preserving model accuracy through diversified cluster representation.
Solution Approach 2:
The system performs partial action by selecting only one training host per cluster to transmit model parameters, rather than requiring all UEs to participate in each communication round. This partial participation is sufficient to maintain model convergence while dramatically reducing communication overhead.
2Quantity of substance
If a subset of UEs is selected for model updates to reduce communication overhead, then the communication load is reduced, but the aggregated model accuracy may degrade due to ineffective selection
Solution Approach 1:
The system performs preliminary clustering of UEs based on data correlation before the federated learning process begins. This preliminary organization ensures that when UEs are selected for model updates, they are chosen from pre-defined clusters with similar data characteristics, guaranteeing effective representation and preventing accuracy degradation.
Solution Approach 2:
The system implements feedback mechanisms where the network node receives model parameters from selected training hosts, aggregates them, and evaluates convergence. This feedback loop allows the system to monitor model accuracy and adjust cluster selections dynamically, ensuring that the subset of selected UEs continues to produce effective model updates.
3Speed
If synchronous model collection is used to ensure all UEs contribute updates, then the convergence speed is maintained, but the communication overhead increases
Solution Approach 1:
The system segments the synchronous model collection process into multiple asynchronous cluster-based sub-processes. Each cluster independently selects and transmits model updates without waiting for other clusters, maintaining overall convergence speed while reducing the simultaneous communication load through temporal and spatial segmentation.
Data Source
AI summary
Systems, methods, apparatuses, and computer program products for cluster based local ML model training host selection in asynchronous federated learning model collection. A method may include transmitting local model parameters following training of a model of at least one network element to a network node to be used to cluster the at least one network element with one or more other network elements. The method may also include training, by one or more network elements selected from the cluster, an aggregated model using the local model parameters. The method may further include transmitting, by the one or more network elements selected from the cluster, updated local model parameters of the at least one network element as a result of the training to the network node.