# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.4 Federated Learning  
Federated learning is a state-of-the-art method that enables the creation of machine learning models using datasets distributed across multiple devices, while maintaining the privacy and security of the data. In the context of smart cities, this approach is particularly relevant as centralized data integration can often result in privacy and security concerns [157].  
For example, in the field of healthcare, centralized data integration for training machine learning models can result in improved performance compared to separate training using data from just one institution [158]. However, this approach is not feasible due to privacy and security issues. In such cases, federated learning provides a solution as it enables training of models without the direct transfer of data, protecting the privacy and confidentiality of the data. This has been demonstrated through comparative analysis of medical data from multiple institutions, which showed that the training effect of the model obtained through federated learning is nearly identical to that of the centralized approach, while preserving the privacy of the data [158].  
Despite its benefits, federated learning is not immune to security concerns. One such vulnerability is the susceptibility to man-in-the-middle attacks, as well as inference attacks aimed at re-identifying data subjects. A recent study [159] has demonstrated this vulnerability through the use of a mobility model called mobility Markov chain, built from the mobility traces observed during the training phase and used to perform the attack during the testing phase. The study used a combination of the closeness between two mobility Markov chain distances to build de-anonymizers that can re-identify users.  
To enhance the privacy and security of federated learning, several approaches have been proposed in the literature. One such approach is the use of secure multiparty computation techniques, which enable secure collaboration between multiple parties without revealing their private data [160]. Another approach is the use of homomorphic encryption, which allows for computations to be performed on encrypted data without the need to decrypt it [161]. These techniques can help to improve the privacy and security of federated learning in smart cities and other applications.  
In recent years, there has also been growing interest in the application of federated learning in various domains, including IoT [162], mobile networks [163] and autonomous driving [164]. These applications highlight the potential of federated learning to address the privacy and security challenges in smart cities and other domains.  
In conclusion, federated learning holds great promise for smart cities, providing a solution to the privacy and security concerns associated with centralized data integration. However, further research is necessary to address the vulnerabilities of this approach, and to enhance its privacy and security features. With its potential to address these challenges, federated learning is poised to play a significant role in the future of data-driven applications in smart cities and beyond.