Federated learning (FL) offers a credible solution for distributed data trading since it could train machine learning models in a distributed manner thereby enhancing data privacy without sharing local data. However, it is still challenging to trade data through FL due to unfair model allocation issues arising from the unreliability of user-provided data. To tackle it, we propose F2UL, a Fairness-aware Federated UnLearning solution that distributes models to users commensurate with their data quality. F2UL is trained in a two-stage (TST) way and contains three main components: 1) Label-free model quality assessment (LMQA) promotes fairness by evaluating models without user-specific data, ensuring uniform assessment standards. 2) Fair model distribution (FMD) addresses the issue of unfair model distribution by allocating models with feature mapping deviation, ensuring that users who contribute low-quality models do not receive enhanced models. 3) User data federated unlearning (UDFU) ensures fairness in model distribution by employing rapid recovery federated unlearning, safeguarding regular users from the adverse effects of low-quality data on model performance. In experiments on CIFAR10, F2UL reduces the low-quality data user accuracy to 9.09% and increases regular users’ accuracy by 3.02%, thereby demonstrating F2UL's capacity to ensure fairness in data trading.