Foresight Ventures: Rational View of Decentralized Computing Power Network

Foresight Ventures: Decentralized Computing Network

Currently, there are two major directions for the combination of AI and Crypto: distributed computing power and ZKML. Foresight Ventures author Yihan analyzes and reflects around decentralized distributed computing networks.

Decentralized distributed computing networks have the greatest demand for AI large-scale model training, but also face the biggest challenges and technical bottlenecks, including complex data synchronization and network optimization issues. In addition, data privacy and security are also important constraints.

Methods such as secure multi-party computation, differential privacy, federated learning, and homomorphic encryption all have their own applicable scenarios and limitations, and no single method can completely solve the data privacy issues in large-scale model training on distributed computing networks. To use ZKP for large-scale distributed computing network training of large models, it will require years of research and development, as well as more academic efforts and resources in this direction. With huge computational costs, these technologies are still not applicable.

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Decentralized distributed computing networks have a better chance of landing in model inference, with a large enough space for predicting future increments. However, it also faces challenges such as communication latency, data privacy, and model security. Compared with model training, the computational complexity and data interaction in inference are lower, making it more suitable for distributed environments.

Reference: https://medium.com/@foresightventures-zh/foresight-ventures-%E7%90%86%E6%80%A7%E7%9C%8B%E5%BE%85%E5%8E%BB%E4%B8%AD%E5%BF%83%E5%8C%96%E7%AE%97%E5%8A%9B%E7%BD%91%E7%BB%9C-7c6e56b0adbe

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