## 4 Types of FL
### Horizontal FL (HFL)  
Machine learning traditionally relies on a centralized training paradigm, where data from various sources are pooled into a single repository. This method simplifies preprocessing, data access, and model evaluation, leading to robust performance and iterative improvements due to the data's homogeneity and volume. However, as the data distribution becomes more complex and concerns about privacy and security grow, new methodologies have emerged. One such approach is horizontal federated learning (HFL), or \"sample-based\" FL, designed for decentralized data. HFL is suitable for scenarios where entities collect data with shared features but from diverse samples [55]. The foundational aspect of HFL is its consistent feature space, maintained across all participating nodes or devices, while individual sample spaces or data records differ. This framework ensures that the data integrity and privacy are safe. In essence, while the data features are consistent among the participants, the need for direct data sharing is obviated, allowing entities to collaboratively refine a shared model [54]. For clearer insight into HFL's applicability, consider two geographically separated hospitals. Both institutions might capture analogous parameters for their patients--age, weight, and blood pressure, to name a few. The divergence arises from the distinct patient demographics that each caters to. In this scenario, HFL acts as a nexus, enabling these hospitals to synergistically enhance their diagnostic models. A notable advantage of this model is the implicit assurance that patient-specific information remains contained within its originating institution, supporting the principles of data privacy, fairness, and accuracy [56]. In this type, data samples from various devices are split horizontally, meaning that each device has the same labels for a subset of features. The objective is to prepare a model that ensures the protection of information in a cooperative way. The usual method of handling AI, which includes concentrating information on a server, presents several challenges--for example, high mailing costs, excessive battery usage, and risks to the protection and security of customer information. FL has gained considerable attention for its ability to build powerful models in a decentralized way, without direct access to customer information, thus ensuring security. Unlike conventional centralized AI, federated learning tends to address the difficulties presented by the non-IID data and imbalanced information presented in real-world applications--for example, mobile phone applications and mode identification trips using non-IID GPS addresses. With the increasing complexity of information gathering and division among organizations, especially when managing sensitive information, disconnected information repositories maintained by individual information owners have become prevalent. This requires the development of AI models that can be prepared without unifying all of the information. Regardless of the potential of HFL, it also faces challenges. One major concern is the potential imbalance of data among participants. Variations in the dataset's volume or diversity between entities could introduce bias in the model's performance. Addressing  
Figure 2: Types of FL. this requires strategies that ensure fair representation and learning, thereby upholding the integrity of the HFL methodology. Regarding security, we note the following. In HFL, data are dispersed among numerous edge devices, like mobile phones or IoT devices, that train local models independently before providing changes to a central server. This decentralized structure provides an opportunity for hostile actors to exploit flaws by inserting poisoned or modified data into the local updates, possibly damaging the global model. Securing the communication connections between the central server and the local devices is crucial in reducing these risks.