## 4 Federated Learning for Healthcare-Based Digital Twin Systems  
In the last few years, machine learning (ML) techniques have seen significant success, which is largely attributed to three key factors that have contributed to their widespread adoption and achievements; these are as follows [70]:  
1. Factor 1: The abundance of big data collected from diverse domains, such as image processing and mobile networking. 2. Factor 2: Recent advancements in computational power and novel learning models. 3. Factor 3: The advancement of machine learning (ML) techniques, which have played a critical role in enabling intelligent behavior in computational systems and enhancing data-driven decision-making across various medical applications. Despite the considerable success of ML, there are challenges preventing its real-time implementation in most domains, primarily concerns regarding data privacy and the compromise of user data confidentiality during central learning, where data needs to be collected by the server. Additionally, the potential of leveraging the vast amount of data and computing resources available at the network edge for learning improvement remains largely untapped. While several companies offer well-trained ML models that can impart knowledge at lower computational costs, significant privacy and confidentiality issues remain unaddressed. To address these challenges, federated learning (FL) has emerged as a promising concept in machine learning (ML) for healthcare-based digital twin systems. In contrast to the traditional central learning approach, FL allows multiple devices to collaboratively learn a shared ML model without the requirement of sharing their data with a centralized server. For example, instead of pooling information from various hospitals or clinics  
Figure 9: Platforms for healthcare-based digital twins. into one place, which can compromise the security of the information, a more dispersed approach could be used; this is what federated learning offers. Each organization's learning becomes a localized activity by utilizing its digital data and only model updates are sent out. This data sharing or exchange process enhances the use of DTs technology in healthcare through improved patient privacy, which is very useful, especially given the sensitive nature of healthcare data. So, this technology creates a personalized virtual model of patients, while federated learning ensures that these models can be trained on diverse and comprehensive data in a privacy-preserving, decentralized manner. By enabling local model updates on individual devices and aggregating them in a privacy-preserving manner, FL ensures data privacy while leveraging the collective intelligence of distributed devices. This decentralized learning paradigm revolutionizes the ML landscape, providing a solution that addresses the limitations of centralized approaches and opens new opportunities for efficient and secure collaborative learning [62]. Trust and privacy preservation are crucial aspects of federated learning, playing a pivotal role in its successful implementation. Federated learning leverages a collaborative approach to model training while ensuring that sensitive data remains decentralized and protected. Trust is essential in federated learning as it involves multiple parties or entities contributing their data for model training. Each participant needs to trust that their data will be used appropriately and that the collaborative process is conducted fairly and securely. Building trust involves establishing clear communication, transparency, and adherence to privacy policies and data usage agreements. It is important to foster an environment where participants feel confident in the privacy and integrity of their information during the federated learning process. Privacy preservation is a main objective of federated learning. The decentralized nature of this approach allows data to remain on local devices or servers, reducing the risks associated with data breaches and unauthorized access.