In the field of federated learning, incentive mechanisms play a crucial role in enticing high-quality data contributors to engage in federated learning and acquire superior models. However, existing research in federated learning often neglects the potential misuse of these incentive mechanisms. Specifically, participants may manipulate their locally trained models to dishonestly maximize their rewards. This issue is thoroughly examined in this study. Firstly, the problem of rewards fraud in federated learning is clearly defined, and the concept of reward-cost ratio is introduced to assess the effectiveness of various rewards fraud techniques and defense mechanisms. Following this, an attack method named the “gradient scale-up attack” is proposed, focusing on manipulating model gradients to exploit the incentive system. This attack method calculates corresponding scaling factors and utilizes them to increase the contribution of the local model to gain more rewards. Finally, an efficient defense mechanism is proposed, which identifies malicious participants by examining the L₂-norms of model updates, effectively thwarting gradient scale-up attacks. Through extensive analysis and experimental validation on datasets such as MNIST, the findings of this research demonstrate that the proposed attack method significantly increases rewards, while the corresponding defense method effectively mitigates fraudulent behavior by participants.