Encoded Spatial Attribute in Multi-Tier Federated Learning

2025-01-10

AI Summary

Modeling geospatial data across multiple granularities is challenging in federated learning due to the difficulty of cross-granularity collaborative training and the impossibility of sharing raw global data. Method: This paper proposes a novel multi-level federated learning framework that integrates spatial attribute encoding into a hierarchical federated architecture. Clients locally encode multi-scale spatial features, and cross-granularity joint modeling is achieved via hierarchical model aggregation—without requiring centralized data sharing. Contribution/Results: The approach enables robust, generalizable multi-granularity spatial prediction models. Experiments demonstrate that the global model achieves 75.62% and 89.52% accuracy on two downstream tasks—even without access to target-granularity training data—significantly improving predictive performance and generalization capability in real-time, heterogeneous geographic scenarios.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Map Data Accuracy
• Model Generalization
• Hierarchical Federated Learning

Innovation

• Multi-level Encoding
• Federated Learning
• Spatial Data Prediction