Encoded Spatial Attribute in Multi-Tier Federated Learning

TL;DR

This paper introduces an encoded spatial multi-tier federated learning framework that enhances geospatial data prediction by encoding spatial attributes across multiple tiers, demonstrating promising accuracy improvements and real-time application potential.

Contribution

The study extends federated learning to multiple tiers with spatial encoding, enabling models to predict at various spatial granularities without extensive retraining.

Findings

Achieved 75.62% and 89.52% accuracy for different spatial tiers.

Demonstrated the effectiveness of spatial encoding in federated models.

Highlighted the importance of multi-tier structure for geospatial data prediction.

Abstract

This research presents an Encoded Spatial Multi-Tier Federated Learning approach for a comprehensive evaluation of aggregated models for geospatial data. In the client tier, encoding spatial information is introduced to better predict the target outcome. The research aims to assess the performance of these models across diverse datasets and spatial attributes, highlighting variations in predictive accuracy. Using evaluation metrics such as accuracy, our research reveals insights into the complexities of spatial granularity and the challenges of capturing underlying patterns in the data. We extended the scope of federated learning (FL) by having multi-tier along with the functionality of encoding spatial attributes. Our N-tier FL approach used encoded spatial data to aggregate in different tiers. We obtained multiple models that predicted the different granularities of spatial data. Our findings underscore the need for further research to improve predictive accuracy and model generalization, with potential avenues including incorporating additional features, refining model architectures, and exploring alternative modeling approaches. Our experiments have several tiers representing different levels of spatial aspects. We obtained accuracy of 75.62% and 89.52% for the global model without having to train the model using the data constituted with the designated tier. The research also highlights the importance of the proposed approach in real-time applications.