> 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/flock-products/fl-alliance.md).

# FL Alliance

A guide to FLock's federated learning client.

<figure><img src="https://742781353-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F1RpcbvTSHzzPwOSUvgKU%2Fuploads%2FcaSeQSRTKBDvufyQAT3f%2F1313.png?alt=media&amp;token=281506a9-d3fc-45cd-8d4a-4ef81357b888" alt=""><figcaption><p>Overview of FL Alliance </p></figcaption></figure>

After AI Arena has optimised the model parameters, they are further refined in FL Alliance with proprietary data. Following our research paper published at IEEE Transactions on Artificial Intelligence (see <https://ieeexplore.ieee.org/abstract/document/10471193>), we provide a robust incentive mechanism incentivise honest behaviours and penalise malicious behaviours of FL participants.

The motivation behind this FL Alliance system is to create a decentralised and secure environment for federated learning. By using random functions to allocate roles and a staking mechanism to ensure commitment, the system promotes trust and reliability among participants. The slash and reward mechanism further incentivises honest behaviour, ensuring the collaborative effort results in a high-quality global model.


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