## 7 Conclusions  
In this paper, we propose a hierarchical blockchain-empowered cloud-edge orchestrated FL (HBCE-FL) architecture aimed at providing a secure and privacy-preserving data analysis solution for IoT users. In HBCE-FL, we design a multi-level ACE-based IoT knowledge sharing approach to address the challenge of user privacy leakage in FL. This scheme enables IoT users to engage in personalized FL based on their unique privacy requirements. Clients and servers in FL are classified into different levels based on privacy requirements and security. The ACE algorithm regulates the flow of knowledge between the users of varying levels and the cloud server, thereby enforcing access control rules. And, blockchain ensures trust and reliability in services among edge servers. The simulation results of the HBCE-FL framework with three security levels show that the multi-level ACE-based IoT knowledge sharing approach enables IoT users with high privacy protection levels to cooperate with other users for distributed data mining. Our framework achieves multi-level privacy-preserving FL with acceptable computation times.  
The proposed multi-level ACE-based personalized privacy-preserving FL framework undergoes evaluation on two widely used datasets, MNIST and CIFAR-10. The average accuracy of our privacy-preserving FL scheme surpasses that of FedAvg without privacy protection by 2.14% and 17.5% on the two datasets, respectively. Additionally, we designed personalized FL for IoT users with non-IID data by having different clients adopt different models. In the non-IID scenario, our scheme achieves average accuracies of 99.51% and 88.21% on the two datasets, respectively, with faster convergence rates. Regardless of  
Figure 5: Performance of the proposed different privacy-preserving FL on different data distributions. (a) Average accuracy; (b) Training loss.  
handling homogeneous or heterogeneous data, IoT users can improve FL performance through secure knowledge access and sharing.  
Our work exclusively focuses on addressing the FL challenge of data security sharing and privacy protection in the context of determined user privacy requirements. In the future, we will design more meticulous and efficient access control rules and ACE algorithms to adapt to the dynamic changes in user privacy requirements. Additionally, we will delve into strategies for handling unreliable or malicious participants.  
Conceptualization, J.W. and J.L.; methodology, J.W.; simulation and analysis, J.W.; writing--review and editing, J.W. and J.L.; supervision, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant U20B2048, Grant U2003206, and Shanghai Engineering Research Center of Cyber and Information Security Evaluation (The Third Research Institute of Ministry of Public Security) KFKT2023-006.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy and ethical concerns.  
The authors declare no conflicts of interest.