Zone-Based Federated Learning Model Selection
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Solution Overview
Problem
Federated learning systems face performance issues due to non-independent and identically distributed (non-IID) data across devices, which can be addressed by grouping devices into zones to improve data distribution.
Innovation Solution
A method for determining zone membership in zone-based federated learning, where devices receive a zone determination function and use parameters like GPS coordinates to determine their zone, allowing them to select and train a federated learning model specific to their zone, and communicate with zone managers for local training and data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If devices are grouped into zones based on geographic location, then model accuracy is improved, but system complexity increases due to zone determination and model selection mechanisms
Solution Approach 1:
The system segments the federated learning network into multiple geographic zones, each with its own zone manager and model. Devices are assigned to zones based on their geographic location (e.g., GPS coordinates), allowing localized training that improves model accuracy for region-specific characteristics while maintaining overall system organization through hierarchical zone management.
Solution Approach 2:
Each zone develops and maintains its own federated learning model tailored to local data characteristics and requirements. This local quality approach allows models to be optimized for specific geographic regions (e.g., local traffic patterns, environmental conditions) while the overall system benefits from distributed, specialized models rather than a single generic model.
2Reliability
If data is partitioned and used within appropriate zones, then privacy preservation is improved, but communication overhead increases due to zone manager interactions
Solution Approach 1:
Zone managers serve as intermediary components that coordinate communication between devices and the central server, as well as between devices in the same zone. This intermediary layer reduces direct communication overhead by handling zone-level aggregation and model distribution locally, while still enabling centralized coordination when needed, thus balancing privacy preservation with communication efficiency.
3Reliability
If devices train on local data within zones, then data localization is improved, but training efficiency decreases due to smaller local datasets
Solution Approach 1:
The system merges training efforts at the zone level by having multiple devices in the same geographic zone collaboratively train a zone-specific federated model. This combining of local training efforts allows each device to benefit from aggregated zone-level data patterns while maintaining data localization, effectively overcoming the limitation of small individual datasets through cooperative zone-based training.
Data Source
AI summary
A processor-implemented method includes receiving, by a user equipment (UE), a zone determination function based on registering for a federated learning process for training a first federated learning model. The method also includes determining, by the UE, a zone membership in accordance with UE parameters and the zone determination function. The method further includes selecting the first federated learning model, by the UE, based on the zone membership. The method includes training the first federated learning model by the UE.