Securing Edge AI: Confidential Computing for Decentralized LLM Inference on Mobile Devices
Introduction Large language models (LLMs) have transformed natural‑language processing, powering everything from chatbots to code assistants. Yet the most capable models—often hundreds of billions of parameters—are traditionally hosted in centralized data centers where they benefit from abundant compute, storage, and security controls. A new wave of edge AI is pushing inference onto mobile devices, enabling offline experiences, reduced latency, and lower bandwidth costs. At the same time, decentralized inference—where many devices collaboratively serve model requests—promises scalability without a single point of failure. ...