# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures
### Federated Learning  
Finally, we look at _federated learning_, which is an approach that was not originally intended to provide privacy. Along with the increased application of machine learning in all kinds of domains, the need for data to train the corresponding models has also increased. While the IoT provides an almost unlimited number of smart devices as data sources, organizational problems arise in terms of the communication overhead required to continuously receive the captured data as well as the central computing resources required to process the data. As IoT-enabled devices become increasingly powerful, the federated learning approach shifts many of the data refinement tasks from the central data processor to the distributed data producers. This includes the entire data preparation phase and large parts of the data processing phase. That is, data producers generate information patterns which are gathered by the data processor. The data processor uses these patterns to create or update global knowledge models that contain the information patterns of all data producers. In other words, federated learning takes aggregation to the next level as data producers exchange only knowledge instead of data with the data processor. From the global models of the data processor, knowledge can also be fed back to the local workers in order to improve their local models [163]. This procedural concept is illustrated in Figure 15.  
Figure 14: Simplified Representation of the Central Idea of Differential Privacy.  
However, federated learning can also contribute to privacy-preserving machine learning. Data privacy is generally regarded as the right of an individual to freely decide how much knowledge he or she wants to disclose about him or herself. This is exactly what federated learning does, as data producers are free to decide which of their data they want to use in the data refinement process and which information patterns they want to include in the knowledge model [164]. By applying privacy filters to the data, differential privacy can be preserved not only for the isolated local models but also for the shared global model. Thereby, a stronger quantifiable privacy protection can be achieved [165]. This protection combined with its distributed computing renders federated learning well-suited for large-scale analyses, such as the DNA sequence classifications presented in Section 2.7[166].  
The key strengths and weaknesses of federated learning approaches from a privacy perspective are the following:  
Strengths:  
* Federated learning is primarily used to efficiently run complex machine learning processes. The preservation of privacy is a beneficial side effect that comes at no additional cost.
* Federated learning enables data subjects to incorporate their data into global machine learning model but to carry out the necessary processing of their private data locally, i.e., under their full control.  
Weaknesses:  
* Due to the complex and non-explanatory nature of the trained models, it is not possible for data subjects to understand what knowledge about them is incorporated into the global model by means of their locally computed models.
* The use of federated learning is limited to certain algorithms and algorithm classes.