## 6 Conclusions and Future Directions  
This paper presented a review of two leading technologies that are leveraged to enhance security and privacy. IoT devices applications include the sharing of massive raw data. Federated learning is integrated to provide intelligence to these low-level devices while preserving the privacy of data by only sharing the trained model. Traditional federated learning is centralized and suffers from security issues of untrusted clients. The added layer of Blockchain integration resolved multiple issues of trust and security.  
This paper reviewed the taxonomy of such integration while briefly describing the elements of each technology. After that, this paper discussed five main security and privacy concerns that the integration of federated learning, Blockchain, and IoT faces. By reviewing the existing literature, this paper compared and measured the integration scope and security and privacy considerations.  
For future work, this survey concludes that the number of existing solutions that address poisoning attacks threats in federated learning and Blockchain integration is still scarce. Although federated learning is designed to protect the privacy of training data, there is still a need to guarantee the security and privacy of the training data. More work needs to be done to address issues of computational and resources limitation in edge devices. There are few existing solutions to address protection against dishonest participants' attempts to poison and alter the shared training process. This paper aims to encourage future work to enhance the security and privacy of both technologies in an IoT environment.  
Our basic aim when preparing this paper was to propose a short and accurate survey that focuses on the most relevant research work in IoT ecosystem data privacy preservation based on joining Blockchain to federated learning. We were motivated by regulations restricting data sharing and privacy concerns. The use of IT solutions in healthcare saves lives but also leads to several problems related, in particular, to the security aspect. Indeed, the poisoning of datasets as well as the falsification of decisions can cause false diagnoses affecting human life. This makes the execution protection of learning models sometimes more important than improving the techniques themselves. We believe that our paper will serve as a key reference for researchers interested in improving solutions based on mixing Blockchain and federated learning in IoT environments while preserving privacy. In the future, the tradeoff between Blockchain cost and the index of privacy preservation may be a hot topic of research. Certainly, a partial amount of data can be shared, and this will affect positively the output of machine-learning-based applications. Another improvement can focus on the clustering of FL clients who can share their data before the final aggregation with others.  
Conceptualization, M.A.A. and T.M.; methodology, M.A.A.; investigation, M.A.A.; resources, M.A.A.; data curation, M.A.A.; writing--original draft preparation, M.A.A.; writing--review and editing, T.M.; visualization, T.M.; supervision, T.M.; project administration, T.M.; funding acquisition, T.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Qassim University, represented by the Deanship of Scientific Research, grant number COC-2022-1-1-J-26046.  
Not applicable.  
The author(s) gratefully acknowledge Qassim University, represented by the Deanship of Scientific Research, on the financial support for this research under the number (COC-2022-1-1-J-26046) during the academic year 1444 AH/ 2022 AD.  
The authors declare no conflict of interest.