# FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing
## 5 Conclusions  
With the rapid development of new generation artificial intelligence technologies represented by deep learning, crowd-sensing computing to support artificial intelligence model training is becoming increasingly popular and rapidly evolving. Crowd-sensing applications, which utilize a large number of mobile terminal devices to establish an open and integrated system capable of processing large amounts of data and information, can easily lead to the leakage of user privacy data, thus posing significant challenges to data security protection in crowd sensing. This paper proposes FedCrow, which is a data security protection method for crowd sensing based on federated learning. FedCrow addresses  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **User Type or Attack** & **Model Average** & **Impact on Model** & **Data** \\  & **Method** & **Accuracy** & & **Leakage** \\ \hline
1 st experiment & Normal user & 96.56\% & No & No \\
2 nd experiment & Unreliable user & 96.23\% & No & No \\
3 rd experiment & Poisoning attack & 96.56\% & No & No \\
4 th experiment & Model attack & 96.57\% & No & No \\
5 th experiment & Inference attack & 96.56\% & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results comparison.  
Figure 12: Model training with a user that conducted an inference attack. (**a**) The similarity of the parameter matrices. (**b**) The model accuracy.  
issues such as malicious user attacks and leakage risks during model data interaction in traditional crowd-sensing applications' federated-learning systems. By constructing an efficient and secure federated-learning framework, FedCrow proposes a crowd-sensing federated-learning data security protection method based on the user contribution level, which evaluates the trustworthiness of a client based on its contribution to the overall model and effectively filters legitimate users, thereby reducing the risk of user privacy data leakage caused by malicious users. Finally, the system's ability to resist malicious user attacks was verified through various attack experiments. Additionally, in order to effectively protect the security of the model data during the interaction process, comparative experiments clearly indicated that reasonable data encryption methods should be adopted for data transmission encryption based on the size of the data in crowd-sensing application scenarios.  
Conceptualization, Y.Y. and J.M.; methodology, Y.Y., J.X. and J.M.; software, L.C.; validation, L.C., J.X. and Y.Y.; formal analysis, Y.Y.; investigation, J.M.; resources, J.M.; data curation, L.C. and J.X.; writing--original draft preparation, Y.Y. and J.X.; writing--review and editing, Y.Y. and J.M.; visualization, L.C.; supervision, J.M. All authors read and agreed to the published version of this manuscript.  
This research work received no external funding.  
Not applicable.  
We used the publicly available dataset provided by Kaggle. This dataset is freely available at [[https://www.kaggle.com/competitions/nus-fintech-recruitment/data](https://www.kaggle.com/competitions/nus-fintech-recruitment/data)]([https://www.kaggle.com/competitions/nus-fintech-recruitment/data](https://www.kaggle.com/competitions/nus-fintech-recruitment/data)), Accessed on 23 August 2022. All other implementation and verification data are included in the article.  
The authors declare no conflicts of interest.