## 6 Defenses against Model and Data Attacks
### Incentivized Federated Learning (FL)  
Incentivized FL is a methodology designed to promote active and constructive participation among distributed clients in training machine learning models while upholding data privacy and fairness standards [126; 127]. Unlike traditional FL, where clients like mobile devices or IoT sensors collaborate to enhance a global model without sharing raw data, incentivization becomes crucial to motivate effective client contributions [128]. This approach ensures that all the participants are invested in the collective learning process, incentivized based on their contributions, which can include model updates, computational resources, or improvements in data quality [129]. These incentives not only boost engagement but also cultivate a cooperative environment where the clients are encouraged to adhere to protocols and perform tasks honestly [48; 130]. The structure of the framework is depicted in Figure 13.  
[PERSON] et al. [131] proposed an incentivized FL framework focused on defending against and managing abnormal clients, essential for enhancing the accuracy and resilience of FL. This framework typically comprises two critical elements: an abnormal client detection module and client incentives. The detection module utilizes techniques such as outlier identification to pinpoint clients exhibiting unusual behavior due to faults or attacks, ensuring that their data updates do not disrupt the global model averages during aggregation. However, outright rejection of updates from abnormal clients risks losing valuable data and reducing their engagement. This is where client incentive mechanisms play a crucial role. These mechanisms encourage continued participation from all clients, including those identified as abnormal, in the subsequent training rounds. By assigning \"credit scores\" based on the client contributions and behaviors, these incentives motivate sustained involvement and improvement over time, thereby discouraging client dropout and promoting overall model integrity.  
Figure 12: Visual representation of blockchain.  
The paper by [PERSON] et al. [132] introduced ISPPFL (Incentive Scheme based Privacy-Preserving Federated Learning), an incentive-based defense framework for avatars in the metaverse. ISPPFL comprises three key components: (1) a privacy risk auditor that evaluates various privacy risks in the FL model; (2) a perturbation generation mechanism that creates perturbed samples using adversarial algorithms to enhance robustness against privacy attacks; and (3) an adaptive incentive mechanism that dynamically adjusts the intensity of noise generation based on the privacy risk indicator, model accuracy, and the accuracy difference between consecutive training epochs. This adaptive incentive mechanism aims to balance privacy protection and model performance. By integrating these components, ISPPFL effectively defends against privacy attacks such as Source Inference Attacks (SIAs) in Horizontal FL and Label Inference Attacks (LIAs) in Vertical FL while maintaining model accuracy. The framework is flexible and adaptable to different types of privacy risks and defense mechanisms in the metaverse context.