## 2 Preliminaries
### Federated Learning  
Federated learning is an innovative approach in machine learning that facilitates the training of models using data from various sources distributed across different locations, such as local data centers or a central server, without the need to share the actual training data. This decentralized approach ensures that personal data remains secure and reduces the risk of data breaches. By enabling the model to gain insights from diverse datasets while preserving data privacy, federated learning offers a promising solution that balances the benefits of collective learning with the protection of individual data [52; 53]. Due to its benefits in data privacy and collaborative ML models, federated learning has attracted great interest and has many applications in the medical sector. Table 2 summarizes some of the main applications and aspects of federated learning in biomedical. Figure 3 visually depicts the primary distinction between federated learning and centralized learning. Several medical facilities or data centers, each with their own local datasets, participated in training a common model which involved federated learning for DTs in the health sector. They only shared model updates, which show, for example, how much the DT model improves while keeping all patient databases isolated and confidential. This integration ensures that the patients' privacy is well maintained, while still enabling the development of highly effective DTs without sharing sensitive medical information on a centralized platform. On the other hand, centralized learning involves a central server that collects and stores all the training data from various devices or sources. The central server \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Year** & **Taxonomy** \\ \hline
[54] & 2022 & Enhance patient quality of life and data privacy using FL in electronic healthcare records (HERs). \\ \hline
[36] & 2023 & Discuss the unique qualities and challenges of FL and provide insights into current approaches and future directions for safeguarding patient data. \\ \hline
[55] & 2023 & Highlight the potential of FL to address data privacy concerns in patient care settings. \\ \hline
[56] & 2023 & Improve fairness in AI models based on electronic health records using FL. \\ \hline
[57] & 2024 & Introduce a federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing. \\ \hline
[58] & 2024 & Propose a comprehensive system enabling healthcare 5.0 using federated learning, intrusion detection, and blockchain. \\ \hline
[59] & 2025 & Explore different disability healthcare solutions through privacy-preserving federated learning. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Federated learning applications in the biomedical sector. performs the training process using the combined dataset. This comparison highlights the decentralized nature of federated learning, where data remain distributed, and the collaborative aspect of the learning process, in contrast to the centralized approach where all data are aggregated and processed by a single entity [53]. Likewise, Figure 4 illustrates the three main types of federated learning currently used:  
1. Horizontal federated learning: This method is utilized when the participating datasets share the same feature space but vary in the sampling space. In other words, the data collected from different sources have similar attributes or characteristics but differ in terms of the instances or samples. 2. Vertical federated learning: This method is applied when the datasets differ in the feature space but have a common sampling space. This means that the data collected from different sources have different attributes or characteristics but share the same instances or samples. Figure 3: Difference between (**a**) centralized learning and (**b**) federated learning. 3.