## 4 K-Anonymity-Based SHFL
### Secure Hierarchical Distributed Architecture
#### 4.1.2 K-Anonymity-Based Regrouping of Anonymous Clusters of SHFL
The proposed SHFL adopts a hierarchical FL approach, in which a centralized server communicates with participating clients who are further connected with the sub-clients. All clients are distributed in clusters using a similarity-based index and privacy-based index to execute distributed FL. In addition, the proposed SHFL introduces a K-anonymity method that hides the location and identity of the clients in the cluster. The communication between the K-anonymous central server and clients is protected using encryption technology. We conducted detailed experiments on the commonly used FL datasets to demonstrate the performance of the proposed SHFL in terms of convergence accuracy. The results prove that the proposed SHFL performs significantly better than the state-of-the-art FL approaches. Communication efficiency is a significant concern in FL, where SHFL needs to minimize the communication cost by using either compressed updates or sparse data, which could be the focus of our future work. Moreover, privacy attacks will be considered in our future work. Conceptualization, S.S.; Methodology, M.A. ([PERSON]) and S.S.; Formal analysis, M.A. ([PERSON]); Writing--original draft, M.A. ([PERSON]); Writing--review & editing, M.A. ([PERSON]), M.A. ([PERSON]), S.F.J., S.S. and M.T. ; Visualization, M.A. ([PERSON]), S.F.J. and S.S.; Supervision, M.T. ; Project administration, S.F.J. All authors have read and agreed to the published version of the manuscript. This research was commissioned by the National Institute of Information and Communications Technology (NICT), JAPAN. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflict of interest regarding the publication of this article. ## References

* (1) [PERSON]; [PERSON]; [PERSON] Internet of things for enterprise systems of modern manufacturing. _IEEE Trans. Ind. Inform._ **2014**, _10_, 1537-1546. * (2) [PERSON]; [PERSON]; [PERSON] Internet of Things for e-Health: An approach to medical applications. In Proceedings of the 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Prague, Czech Republic, 29-30 October 2015; pp. 1-5. Figure 7: Convergence comparison of the proposed SHFL with state-of-the-art techniques, (**a**) comparison accuracy over MNIST; (**b**) comparison loss over MNIST. [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Application and assessment of internet of things toward the sustainability of energy systems: Challenges and issues. _Sustain. Cities Soc._**2020**, _53_, 101957. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] A design characteristics of smart healthcare system as the IoT application. _Indian J. Sci. Technol._**2016**, \(9\), 52. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] p. A Lightweight and Robust User Authentication Protocol with User Anonymity for IoT-Based Healthcare. _CMES-Comput. Model. Eng. Sci._**2022**, _131_, 307-329. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. _Future Gener. Comput. Syst._**2018**, _82_, 375-387. [CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] Federated internet of things and cloud computing pervasive patient health monitoring system. _IEEE Commun. Mag._**2017**, _55_, 48-53. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON];[PERSON] [PERSON] i-MsRTRM: Developing an IoT based Intelligent Medicare system for Real-Time Remote Health monitoring. In Proceedings of the 2016 8 th International Conference on Computational Intelligence and Communication Networks (CICN), Tehri, India, 23-25 December 2016; pp. 641-645. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring. _IEEE Trans. Mob.