Edge Driven Synthesis Mobility Architecture: Integrating Federated AI and GPU Acceleration for Adaptive Mobile Intelligence

The proliferation of mobile devices and edge computing infrastructure has intensified demands for high-performance data processing at the network edge. Contemporary mobile applications and AI in financial services, healthcare, and intelligent transportation systems increasingly require real-time analytics capabilities that traditional CPU-based architectures cannot adequately provide. This Paper focus Edge-driven Synthesis Mobility Architecture, a GPU-accelerated mobile edge computing framework that addresses fundamental limitations in current mobile intelligence systems: computational asymmetry, data fragmentation, and ephemeral semantic understanding inherent in rule-based agents and conventional adaptive intelligence approaches. Edge-Driven Synthesis Mobility Architecture: introduces a novel four-grid architectural pattern—Sentinel, Fusion, Signal, and Pulse—built upon Apache Iceberg and the NVIDIA RAPIDS ecosystem. The architecture integrates FSX Lustre with GPU Direct Storage (GDS) to eliminate CPU bottlenecks in data transfer, Apache Iceberg for unified table format management, and RAPIDS Polars for distributed GPU-accelerated processing. By combining GPU-native data platforms with federated learning and generative AI, it establishes persistent, context-aware mobile intelligence rather than transient computational responses. Focused comprehensive evaluations using the TPC-DS decision support benchmark suite across data scales ranging from 100GB to 10TB on AWS p4d.24xlarge instances equipped with eight A100 GPUs. Comparative analysis against Apache Spark and Databricks revealed substantial performance advantages: average speedups of 12.4× and 9.5× respectively, with 10-20× improvements observed across individual query patterns. End-to-end latency decreased by 42% (from 284ms to 165ms, p < 0.001), with 89% of queries completing in under 100ms. User studies with 47 participants demonstrated statistically significant improvements in satisfaction scores (6.1 vs. 4.5, p < 0.001) and task completion rates (92% vs. 76%, p < 0.01). These results establish that treating semantics as a persistent system property rather than ephemeral query artifacts enables qualitatively new capabilities in mobile intelligence architecture, opening pathways for next-generation human-computer interaction at the edge.