> For the complete documentation index, see [llms.txt](https://docs.flock.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flock.io/what-is-flock/architectural-breakdown/system-design.md).
# System Design
FLock's system logic is comprised of three major components, namely, AI Arena, FL Alliance and AI Marketplace.
Figure1. FLock's system logic.
As shown in Figure 1, upon a task creation, the model is first trained and validated in ***AI Arena***, a blockchain-based decentralised training platform, and then optionally further fine-tuned in ***FL Alliance*** using participants’ local data. Finally, the model is deployed by applications in the ***AI Marketplace***, where feedback will be used to further improve the model.
Figure2. FLock's system design.
Specifically, as shown in Figure 2, when a task is first created in AI Arena, it is first trained by training nodes. These nodes then submit their models to validators, who evaluate and propose scores for each submission. The validators reach a consensus on these scores to determine the ranking of the submitted models. The consensus model can then be assigned to FL clients, who fine-tune and improve it using their local data, resulting in the FL global model. The AI Arena consensus model or the FL global model can be deployed and hosted in the AI Marketplace, providing interfaces to various applications. AI Arena training nodes, validators and FL clients need to stake to participate the system, and will be rewarded based on their performance.
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