# GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
## 4 The Proposed Scheme
Additionally, we will conduct more detailed and intuitive evaluations of the algorithm's performance using datasets from various fields, including healthcare and finance. Conceptualization, Y.K. ; methodology, Y.K. and Y.C. ; experiments and data curation, Y.K., L.F. and X.L. ; writing--original draft preparation, Y.K. ; writing--review and editing, Y.K. and X.L. ; supervision, W.T. and J.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China, grant number 62272199. **Data Availability Statement:** The data presented in this study are available upon request from the corresponding author due to privacy restrictions. **Conflicts of Interest:** The authors declare no conflicts of interest. ## References

* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Privacy-preserving federated learning with malicious clients and honest-but-curious servers. _IEEE Trans. Inf. Forensics Secur._**2023**, _18_, 4329-4344. [CrossRef]
* [PERSON] and Loeb (2002) [PERSON]; [PERSON] The economics of information security investment. _ACM Trans. Inf. Syst. Secur. (TISSEC)_**2002**, \(5\), 438-457. [CrossRef]
* [PERSON] and [PERSON] (2005) [PERSON]; [PERSON] The economic incentives for sharing security information. _Inf. Syst. Res._**2005**, _16_, 186-208. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Communication-efficient federated learning. _Proc. Natl. Acad. Sci. USA_**2021**, _118_, e2024789118. [CrossRef] [PubMed]
* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Efficient and secure federated learning for financial applications. _Appl. Sci._**2023**, _13_, 5877. [CrossRef]
* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A privacy-preserving hybrid federated learning framework for financial crime detection. _arXiv_**2023**, arXiv:2302.03654. * _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A survey of federated learning from data perspective in the healthcare domain: Challenges, methods, and future directions. _IEEE Access_**2023**, _11_, 45711-45735. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Federated learning for healthcare informatics. _J. Healthc. Informatics Res._**2021**, \(5\), 1-19. [CrossRef]
* _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Federated learning for vision-and-language grounding problems. In Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, 7-12 February 2020; Volume 34, pp. 11572-11579. * _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Communication-efficient learning of deep networks from decentralized data. In Proceedings of the Artificial Intelligence and Statistics, PMLR, Fort Lauderdale, FL, USA, 20-22 April 2017; pp. 1273-1282. * _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Federated learning in mobile edge networks: A comprehensive survey. _IEEE Commun. Surv. Tutorials_**2020**, _22_, 2031-2063. [CrossRef]
* _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Reliable federated learning for mobile networks. _IEEE Wirel. Commun._**2020**, _27_, 72-80. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Federated learning for internet of things: A comprehensive survey. _IEEE Commun. Surv. Tutorials_**2021**, _23_, 1622-1658. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A survey on federated learning. _Knowl.-Based Syst._**2021**, _216_, 106775. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] EDEN: Enforcing location privacy through re-identification risk assessment: A federated learning approach. _Proc. ACM Interact. Mob.