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.