Accelerating Edge Intelligence Through Quantized Model Deployment on Distributed Peer‑to‑Peer Mesh Networks
Table of Contents Introduction Fundamental Concepts 2.1. Edge Intelligence 2.2. Peer‑to‑Peer Mesh Networks 2.3. Model Quantization Why Quantization Is a Game‑Changer for Edge AI Designing a Distributed P2P Mesh for Model Delivery End‑to‑End Quantized Model Deployment Workflow Practical Example: Deploying a Quantized ResNet‑18 on a Raspberry‑Pi Mesh 6.1. Setup Overview 6.2. Quantizing the Model with PyTorch 6.3. Packaging and Distributing via libp2p 6.4. Running Inference on Edge Nodes Performance Evaluation & Benchmarks Challenges and Mitigation Strategies 8.1. Network Variability 8.2. Hardware Heterogeneity 8.3. Security & Trust Future Directions 9.1. Adaptive Quantization & On‑Device Retraining 9.2. Federated Learning Over Meshes 9.3. Standardization Efforts Conclusion Resources Introduction Edge intelligence—the ability to run sophisticated machine‑learning (ML) inference close to the data source—has moved from a research curiosity to a production necessity. From autonomous drones to smart factories, the demand for low‑latency, privacy‑preserving AI is exploding. Yet, edge devices are typically constrained by compute, memory, power, and network bandwidth. Traditional cloud‑centric deployment patterns no longer satisfy these constraints. ...