## 2 Preliminaries
### Federated Learning
Federated transfer learning: This method is employed in situations where the datasets not only differ in the feature space but also have distinct sampling spaces. In other words, the data collected from different sources have both different attributes or characteristics and different instances or samples. These three types of federated learning allow for collaboration and model training across disparate datasets while accounting for variations in the feature space, sampling space, or both. In terms of individuals' health data, horizontal, vertical, and federated transfer learning promote the development of models from various medical databases, supporting the development of highly personalized virtual models of patients. Horizontal federated learning enables entities with similar infrastructural organization, such as hospitals with similar types of patient records, to collaborate with one another. Conversely, within vertical federated learning, a group of different but interrelated entities can merge their data for analysis without any overlap of the types of data they possess. For example, hospitals may have patient records, while drug manufacturers may have information on the genetics of those patients. This section examines the comparison between federated learning and centralized learning approaches, particularly in biomedical applications. These two paradigms have distinct characteristics when applied to sensitive data, such as patient records, which are subject to stringent privacy regulations like the Health Insurance Portability And Accountability Act (HIPAA) [60] and the General Data Protection Regulation (GDPR). 1. Privacy: * Federated learning (FL) inherently supports privacy-preserving machine learning by allowing data to remain on local devices (e.g., hospitals or clinics), while sharing only model updates. This minimizes the exposure of sensitive data to potential breaches or misuse. * In contrast, centralized learning requires the aggregation of all data into a central server, increasing the risk of data leakage during transmission or storage. Even with encryption and secure storage, centralized repositories remain a high-value target for cyberattacks. Figure 4: Three main types of federated learning: (a) horizontal, (b) vertical, and (c) federated. Performance: * Centralized learning typically offers better model performance since it has access to a full dataset, providing comprehensive training on all available data. This results in more accurate models, particularly when dealing with diverse datasets. However, this advantage comes at the cost of privacy and data-sharing concerns. * On the other hand, federated learning often deals with non-independent and identically distributed (non-IID) data across nodes, which may introduce biases and reduce model performance. Additionally, due to decentralized data, the models may converge more slowly or reduce their effectiveness as a result of generalization. * Implementation Challenges:
* Federated learning faces significant technical challenges in biomedical applications, such as heterogeneity in data quality, device resources, and network connectivity across different hospitals or institutions. Ensuring secure model updates, synchronization, and efficient communication without compromising patient confidentiality are hurdles that need to be addressed. * Centralized learning, although simpler to implement from a model-building perspective, demands sophisticated security protocols to protect the stored centralized data. Moreover, compliance with clinical regulations when sharing sensitive information poses further implementation challenges. Overall, while federated learning offers promising privacy advantages, centralized learning may still outperform it in terms of model accuracy. A hybrid or balanced approach could potentially bridge the gap by incorporating privacy-preserving techniques such as differential privacy or secure multi-party computation within centralized systems.