> For the complete documentation index, see [llms.txt](https://deai.gitbook.io/deai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://deai.gitbook.io/deai/landscape/challenges.md).

# Challenges

Achieving a fully Decentralized AI (DeAI) framework entails overcoming a series of intricate challenges:

* **Privacy Preservation**:  Preserving privacy while effectively utilizing data for model training poses a significant challenge. It necessitates sophisticated techniques to assess data quality and train models without divulging sensitive data, ensuring that model outputs do not contain privacy-compromising information.
* **Incentivization Mechanisms:** Encouraging data providers to contribute high-quality data requires the implementation of robust incentivization mechanisms. These mechanisms should adequately compensate data contributors while also rewarding model trainers and other participants for their valuable contributions.
* **Verification of Computation:** Establishing trust among participants in the DeAI network demands reliable mechanisms to verify computations. These mechanisms should ensure that all participants adhere to the agreed-upon protocols and accurately execute computations, thereby enhancing the overall integrity of the network.
* **Network Scalability**: Overcoming bandwidth limitations is crucial for enabling the complete implementation of decentralized computing power, particularly for large-scale models with billions of parameters and the network with thousands of remote nodes. Addressing scalability challenges involves optimizing network architectures, improving communication protocols, and leveraging advanced networking technologies.