> For the complete documentation index, see [llms.txt](https://azen-protocol.gitbook.io/azen-gitbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://azen-protocol.gitbook.io/azen-gitbook/azen-protocol/core-architecture-and-key-functions.md).

# Core Architecture & Key Functions

## Key features

* Dynamic Fractional NFTs (dfNFTs) for Computation, AI & Applications
  * Converts computing resources, AI models, and applications into dfNFTs, enabling fractional ownership, leasing, and efficient execution.
  * Provides interoperability across decentralized computing environments, ensuring seamless deployment of AI-powered workloads.
* Smart Contract-Driven Resource Orchestration
  * Automates the allocation and execution of AI, applications, and computational tasks.
  * Uses predictive AI-based scheduling to enhance resource utilization and reduce inefficiencies.
* Modular Computation & AI Integration
  * Supports decentralized execution of AI inference, model training, and dApp services.
  * Allows applications to access distributed computing resources on a pay-per-use basis.
* Cloud-to-Edge AI Computing & Execution
  * Enables AI & application workloads to dynamically shift between cloud, fog, and edge layers.
  * Reduces latency for real-time AI model execution and decentralized application processing.
* AI-Powered Security & Privacy Enhancements
  * Use zero-knowledge proof (ZKP) and homomorphic encryption for secure computation.
  * AI-driven anomaly detection enhances trust and security in decentralized networks.
* Decentralized AI, Application & Computation Marketplace
  * Users can monetize computing, AI models, and application services.
  * Smart contracts facilitate trustless transactions and execution guarantees.
* Decentralized Data Collection & Processing for AI
  * Data sources are tokenized and secured via smart contracts, ensuring privacy and trust.
  * AI models access real-time, distributed data for enhanced learning and adaptive intelligence.
  * Supports privacy-preserving machine learning (PPML), allowing AI models to train without compromising user data security.