Hybrid AI Modal for Edge Computing in 5G

Growing next generation technologies include autonomous driving, smart healthcare systems, and augmented reality provide massive amounts of data that need to be consistently and fast handled. Expectations of ultra-low latency and tremendous bandwidth have surged sharply with the deployment of 5G networks. Although conventional cloud computing provides a lot of processing capability, its inherent delay from centralized architectures makes it difficult to fulfill the real-time needs of these applications. Moving computation closer to data sources gives edge computing a potential answer. The edge does, however, have several drawbacks as well: limited processing resources, changing device circumstances, and higher security hazards. We offer a hybrid AI-driven architecture specifically for 5G edge settings in order to handle these difficulties. The approach deliberately combines lightweight machine learning and deep learning modules to dynamically allocate tasks across edge, fog, and cloud tiers. It combines important technologies like a trust-aware approach to filter unreliable edge nodes, reinforcement learning for intelligent job offloading, and federated learning for privacy protection. Here we created and simulated the whole architecture with MATLAB. Early-exit logic in a modular hybrid AI system with RL-based offloading agent, trust score evaluation, and federated model aggregation forms part of our approach. To see system latency, bandwidth usage, and performance changes under dynamic traffic, we also created waveform graphs. Simulation results showed that the proposed hybrid model lay a strong basis for next-generation intelligent edge systems in real-world 5G deployments since it outperformed both edge-only and cloud only configurations in terms of response time, scalability, and resilience to adversarial conditions