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 ), 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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# Agent Instructions: Querying This Documentation
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