# Decentralized federated AI In the discussion about [decentralized visual computing](/inferix-whitepaper/decentralized-visual-computing.md) service, we have introduced the physical GPU network used for graphics rendering. We discuss now how we utilize this GPU network for AI training and inference. These processes are essential items of Inferix's Phase 2 strategy, aligning with the core principles of Web3: openness, decentralization, self-governance, and diversity. Currently, AI advancements are predominantly driven by industry giants like Google and OpenAI, relegating most users to passive roles. This situation runs counter to the principles of Web3 and DePIN. To bridge this gap, we propose an application framework for deploying federated learning models on the Inferix GPU infrastructure in the following sections. This framework is designed not only to reshape the existing landscape but also to elevate the intelligence of the evolving DePIN ecosystem. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.inferix.io/inferix-whitepaper/decentralized-federated-ai.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.