## 6 Conclusions and Future Work  
In this paper, we propose a unified framework called TAIM (Trust-Aware Incentive Mechanism) to address key challenges in federated learning in edge computing environments, including client heterogeneity, incentive imbalance, and adversarial robustness. TAIM integrates dynamic multi-dimensional trust modeling and incentive game theory, jointly modeling participation reliability, gradient consistency, and contribution effective  
Figure 8: Accuracy impact under different \(k\) values and trust component weights. \begin{table}
\begin{tabular}{l c} \hline
**Configuration** & **Accuracy** \\ \hline Full TAIM & 75.2 \\ w/o \(\phi\) (Participation Frequency) & 70.5 \\ w/o \(\psi\) (Gradient Consistency) & 67.9 \\ w/o \(\omega\) (Contribution Effectiveness) & 69.4 \\ \hline \end{tabular}
\end{table} 
Table 5: Ablation study on trust dimensions (CIFAR-10 accuracy, %). ness. Based on these trust scores, a Stackelberg game-based incentive allocation strategy and a trust-guided soft aggregation algorithm are designed. TAIM achieves a balance between incentive rationality, fairness, and system robustness. Experimental results show that TAIM consistently outperforms baseline methods across various non-IID data and adversarial settings. It improves model accuracy (up to +6.1%), reduces robustness degradation (kept within 3%), and maintains low False-Positive Rates and stable fairness. Moreover, the proposed soft filtering strategy enhances system security while preserving client diversity, enabling long-term trust evolution and reputation recovery. Despite the progress made, several limitations remain to be addressed in future work:  
First, the current trust evaluation process relies on centralized server control, posing potential risks of data linkage leakage and single-point failure. Future research may explore decentralized technologies such as blockchain and secure multi-party computation (SMC) to enhance privacy and system resilience. Second, the client response function in the Stackelberg game is simplified, assuming fully rational and immediate reactions. It does not account for constrained strategy spaces or delayed behavior. Future extensions may incorporate game learning methods (e.g., Q-learning Stackelberg) or evolutionary game theory to better capture real-world client dynamics. Third, this work primarily focuses on single-task, unimodal scenarios. The applicability of TAIM to multi-modal federated learning (e.g., joint modeling of vision and language) remains unexplored. Future studies should investigate trust modeling across modalities to support collaborative heterogeneous tasks. Lastly, the current trust mechanism is not integrated with differential privacy (DP), raising potential privacy leakage concerns. Future work may investigate how to ensure trust computation effectiveness and robustness under DP budget constraints. In summary, TAIM provides an effective trust-driven solution for building future-ready federated learning systems characterized by openness, dynamism, and strategic participation. Ongoing efforts will focus on enhancing the generality, security, and distributed capability of the mechanism to enable wide deployment of trustworthy federated intelligence. Literature review, J.X. ; methodology, J.X. ; data curation, L.J. ; writing--original draft preparation, J.X. ; writing--review and editing, C.Z. and C.S. ; supervision, C.S. ; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript. This work is supported by JSPS Grant-in-Aid for Scientific Research (C) 23K11103. The datasets used in this study are publicly available. FEMNIST is provided by the LEAF benchmark suite ([[https://leaf.cmu.edu/](https://leaf.cmu.edu/)]([https://leaf.cmu.edu/](https://leaf.cmu.edu/)), accessed on 20 April 2025).