## 2 Related Work
### Incentive Mechanism Design in Federated Learning  
In practical deployments, federated learning involves numerous clients with varying resource capabilities. Due to privacy concerns and resource consumption, clients are often reluctant to participate consistently and contribute high-quality updates [19; 20]. Therefore, designing effective incentive mechanisms to enhance participation and contribution has become a key research challenge [21; 22]. A comprehensive survey by [PERSON] [23] categorizes these mechanisms, highlighting the prominence of game theory, auction theory, and contract theory in addressing the challenge of motivating self-interested clients.  
Early studies primarily focused on resource-driven incentive models. For example, [PERSON] et al. [24] proposed a Stackelberg game-based incentive model that rewards clients  
Figure 3: Taxonomy of research directions in trustworthy federated learning.  
based on the quality of uploaded models rather than data volume or training time. While such methods can improve system efficiency to some extent, they often ignore heterogeneity in model quality and participation behavior, leading to excessive rewards for strategically behaving clients who appear to contribute.  
To enhance fairness and robustness, some works introduced game theory and marginal contribution analysis. [PERSON] et al. (2015) and [PERSON] et al. (2016) designed demand-based reward allocation strategies using Shapley value estimation to evaluate each client's marginal improvement to the global model. Different game-theoretic approaches have also been explored; for instance, [PERSON] et al. (2017) designed an incentive auction for heterogeneous client selection, focusing on creating a market-based environment for efficient resource allocation. While effective, auction mechanisms differ from our approach by emphasizing competitive bidding rather than long-term trust cultivation. However, computing Shapley values is computationally intensive and lacks robustness in adversarial or non-ideal environments.  
Recognizing the limitations of static or purely contribution-based metrics, recent works have shifted towards evaluating client behavior over time. For example, [PERSON] et al. (2018) proposed a method to predict client contributions by evaluating past behaviors, aiming to proactively select more reliable participants. This predictive approach complements our reactive trust-scoring mechanism, which assesses credibility after each round to dynamically adjust rewards and aggregation weights. Moreover, some research has explored reinforcement learning for dynamic incentive strategy generation. For instance, [PERSON] et al. (2019) introduced a deep reinforcement learning algorithm for incentive-based demand response which continuously optimizes interaction strategies using client states and feedback signals under incomplete information. Although such approaches improve adaptability, they often rely on global reward signals and struggle to capture individual trustworthiness or defend against strategic manipulation. The growing complexity and diversity of these mechanisms also underscore the need for standardized evaluation frameworks, as addressed by platforms like FLWB (2018), which facilitate reproducible performance comparisons of FL algorithms.  
In summary, current incentive mechanisms lack effective modeling and utilization of client behavioral credibility, resulting in misallocated or abused rewards. This study incorporates trust scores into the game-theoretic incentive function to construct a behavior-driven resource allocation mechanism, aiming to enhance system security and participation stability.