## 2 Background
### Communication  
Another vital element of FLSs is communication, which involves the exchange of data between the central server and participants, including devices, clients, or edge nodes. Effective and safe communication is necessary for FLSs to function well. The following provides an analysis of the various components related to communication within the taxonomy of FLSs. * Communication patterns: Communication patterns in FLSs concern how participants, including diverse devices or entities, interact with one another and the central server during the collaborative learning procedure. These patterns play a crucial role in facilitating the effective and secure transmission of data and updates to models. In FLSs, the following communication patterns are considered to be the most common: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Authentication Mechanisms** & **Strengths** & **Weaknesses** \\ \hline User credentials authentication & Simple, widely understood and used. & Vulnerable to password-based attacks if weak passwords are used. \\ \hline Biometric authentication & Highly secure, unique to individuals, eliminates the need for passwords. & Hardware requirements (e.g., fingerprint scanners), potential false positives/negatives. \\ \hline Token-based authentication & Reduces reliance on passwords, enhances security for multiple requests. & Requires secure token storage and transmission mechanisms. \\ \hline Certificate-based authentication & Strong security, verified by certificate authorities. & Complex certificate management, reliance on a trusted certificate authority. \\ \hline Multi-factor authentication & Adds an extra layer of security, even if one factor is compromised. & User inconvenience, requires additional verification steps. \\ \hline OAuth and OpenID Connect & Widely adopted, standardized, secure token-based authentication. & Requires integration and understanding of protocols. \\ \hline Device-based authentication & Ensures device authenticity, useful for Internet of Things devices. & Complex device management, potential security vulnerabilities. \\ \hline Role-based access control & Granular control over user permissions, scalable for large systems. & Initial setup complexity, requires ongoing management. \\ \hline Continuous authentication & Provides real-time security monitoring, identifies and responds to anomalies. & Requires sophisticated monitoring tools, potential false positive/negative issues. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the main authentication mechanisms within Federated Learning systems. * Client-server communication: In this communication pattern, the participants' devices establish direct contact with a central server, through which they transmit their changes and receive aggregated model parameters. It is frequently observed in situations where participants possess restricted computational capabilities and depend on a central server to aggregate models. * Peer-to-peer communication: In this setting, users directly communicate with one another, facilitating the exchange of model updates or aggregated information without needing a central server to mediate the process. Utilizing decentralized environments is advantageous as it allows players to establish direct connections, minimizing latency and decreasing reliance on a central server. * Hierarchical communication: In this pattern, participants are systematically grouped into hierarchical structures, wherein updates are initially consolidated at lower levels before being transmitted to higher levels for additional consolidation. This approach exhibits scalability, particularly in the context of massive federated networks, enabling effective aggregation at several hierarchical levels [36]. * Federated architecture with cloud offloading: Participants carry out the preliminary computations at their respective locations and then send the more intensive computations (such as aggregation) to a central server hosted in the cloud.