Large language models (LLMs) have significantly advanced automated code generation and debugging, facilitating powerful multi-agent coding frameworks. However, deploying these sophisticated models on resource-constrained edge devices remains challenging due to high computational demands, limited adaptability, and significant privacy risks associated with cloud-based processing. Motivated by these constraints, we propose \textbf{Edge Code Cloak Coder (ECCC)}, a novel edge-cloud hybrid framework integrating lightweight quantized LLM with robust AST-based anonymization and edge-side privacy validation. ECCC enables high-performance, privacy-preserving LLM capabilities on consumer GPUs, anonymizing user code before securely delegating abstracted tasks to cloud LLMs. Experimental evaluations demonstrate that ECCC achieves competitive correctness (within 4–5pp of the GPT-4-based frameworks) and a perfect privacy score of 10/10, effectively balancing functionality and security for sensitive and proprietary code applications.