Towards an advancement of federated learning framework: A unified approach for multi-tier spatial encoding, spatio-temporal modeling, and multi-global server architectures

Abstract

As machine learning (ML) applications continue to expand into distributed environments, ensuring privacy, efficiency, and scalability in model training has become a critical challenge. Federated Learning (FL) enables decentralized model training while preserving data privacy, but traditional frameworks struggle with spatial dependencies, real-time adaptation, and scalable aggregation. This research integrates three key advancements to enhance FL: Spatial Encoding and Multi-Tier Aggregation, Real-Time Spatio-Temporal Modeling, and Multi-Global Server Architectures. By integrating spatial encoding, FL improves prediction accuracy by an average of 6% in location-sensitive applications, while multi-tier aggregation addresses the geospatial relation. Spatio-temporal modeling combines spatial learning and temporal dependencies and reduces mean square error by 15% on average, enabling FL to adapt to real-time data streams in various applications. A multi-global server architecture enhances fault tolerance and system resilience. This unified FL framework is efficient, improving model accuracy and system stability in large-scale Artificial Intelligence (AI) applications.