## 3 General Opportunities, Challenges, and Applications
### Main Challenges  
The decentralized design of FL, which emphasizes data privacy and distributed learning, differs from that of traditional, centralized machine learning models, introducing novel challenges at every stage. Pioneering research has been instrumental in outlining the numerous challenges that affect FL, and it is imperative to understand these in detail if we are to exploit the full potential of FL. While federated learning offers exciting possibilities, it represents a significant shift from traditional methodologies. This innovation, albeit promising, introduces new layers of complexity, especially in implementation. Techniques that worked well in centralized systems might not be directly applicable any longer. The expansion of FL across large and diverse networks brings challenges related to synchronization, data standardization, and real-time collaboration. Moreover, the varying capabilities and potential failures of devices, as previously mentioned, add to these complexities. To fully leverage FL, researchers and practitioners need to innovate, adapt, and create new strategies suited to this model. The inherent design of FL promotes data security. However, the very nature of distributed systems introduces multiple points of potential vulnerability. Every interaction and every data exchange, regardless of whether it is minimal, needs \begin{table}
\begin{tabular}{l l l} \hline \hline
**Application Area** & **Description** \\ \hline Organizations & \(\bullet\) & FL offers a new framework for healthcare and finance sectors in managing vast amounts of confidential data [29]. \\  & \(\bullet\) & Decentralized modeling allows insights without exposing individual data, aligning with GDPR and HIPAA regulations. \\ \hline Internet of Things (IoT) & \(\bullet\) & FL applications in the IoT universe enhance functionality through decentralized learning [30]. \\  & \(\bullet\) & Security benefits are achieved through data decentralization, making devices more efficient and adaptive. \\ \hline Healthcare & \(\bullet\) & FL addresses the intersection of data-driven insights and ethical considerations in healthcare [31]. \\  & \(\bullet\) & Decentralized processing on healthcare databases preserves patients’ data integrity while pooling insights for industry-wide patterns [32]. \\ \hline Personalized Advertising & \(\bullet\) & FL decentralizes the learning process, allowing advertising platforms to glean user insights without accessing granular data [33]. \\  & \(\bullet\) & Ensures robust personalization while respecting user privacy, shifting digital advertising strategies [34; 35; 36]. \\ \hline Automotive Sector & \(\bullet\) & FL facilitates on-device training for autonomous vehicles, revolutionizing real-time, data-driven decision-making [37]. \\  & \(\bullet\) & Potential to accelerate the transition to more efficient and safer self-driving vehicles [38]. \\ \hline Defense Against Financial & \(\bullet\) & FL serves as a potent tool for the banking sector in preempting financial malpractice [39]. \\  & \(\bullet\) & Collaborative model training without sharing raw data supports predictive accuracy and customer trust. \\ \hline Insurance and FL & \(\bullet\) & FL enables multi-party computation and insight sharing without the direct transfer of raw data in insurance operations [40; 41; 42]. \\  & \(\bullet\) & Optimizes risk models while ensuring that individual data rights remain uncompromised [43]. \\ \hline Military and Police Forces & \(\bullet\) & FL enhances machine learning models for law enforcement agencies without sharing raw data. \\  & \(\bullet\) & Military adoption of FL shifts the focus from central servers to local storage on edge devices, ensuring secure, efficient data exchange. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)to be fortified against breaches. Integrating secure multi-party computation (SMC) with FL is one way to enhance the privacy and security in federated settings.