Zone-Based Federated Learning Model Management
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing model updates for deep neural networks across multiple edge devices, particularly in customizing models based on inherent similarities and differences among devices.
Innovation Solution
The method involves a zone-based federated learning approach, where a first network device associated with a zone model receives a global model, transmits it to user equipment (UEs) within its zone, collects weights from these UEs, updates the zone model, and then transmits the updated model back to the UEs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized global model is used for all edge devices, then system complexity is reduced, but model accuracy and relevance to specific device groups deteriorates
Solution Approach 1:
The patent divides the unified global model into multiple zone-specific models, where each zone model is trained on data from edge devices in a specific geographic or logical zone. This segmentation allows each model to be optimized for local characteristics while maintaining overall system coordination through the global model, thereby improving model accuracy for specific device groups without significantly increasing system complexity.
Solution Approach 2:
The patent implements local quality by training zone models using federated learning on local data from edge devices in each zone, allowing each zone model to adapt to local data distributions and device characteristics. This ensures that each zone model has high relevance and accuracy for its specific group of devices while the global model maintains overall system coherence.
2Manufacturing precision
If customized zone models are trained for each group of edge devices, then model accuracy and relevance improves, but system complexity and computational overhead increases
Solution Approach 1:
The patent implements a multi-layer model management system where the global model serves multiple zones and zone models serve specific device groups. Each model layer performs multiple functions: the global model provides overall system coordination and can serve as a fallback for all zones, while zone models provide specialized optimization for specific groups. This universal structure manages complexity through hierarchical reuse rather than independent management of all models.
Solution Approach 2:
The patent performs preliminary action by pre-training zone models using federated learning on local data before deployment to edge devices. This preliminary training phase allows zone models to adapt to local characteristics in advance, reducing the need for complex real-time adjustments and simplifying ongoing model management during actual operation.
3Reliability
If federated learning is used for model training, then data privacy is improved, but training time and computational resources increase
Solution Approach 1:
The patent segments the federated learning process into zone-level training rounds, where each zone trains its model independently on local data without sharing raw data with other zones or the central server. This segmentation reduces the overall training time by allowing parallel processing across zones while maintaining data privacy, as each zone's training can proceed independently without waiting for other zones.
Data Source
AI summary
A method for managing model updates by a first zone server, associated with a first zone model of a plurality of zone models, includes receiving a global model from a global server associated with the global model. The method also includes transmitting the global model to user equipment (UEs) in a first group of UEs associated with the first zone model. The method further includes receiving, from one or more UEs in the first group, model updates associated with the global model based on transmitting the global model. The method further includes transmitting, to the global server, an average of the model updates received from the one or more UEs. The method also includes updating the global model to generate the first zone model based on the model updates. The method further includes transmitting the first zone model to one or more UEs in the first group.