## 2 Foundational Concepts
### Federated Learning  
FL enables collaborative training on decentralized data [9]. The most widely known FL algorithm is Federated Averaging (FedAvg) [9; 38; 39]. In each communication round t, local clients receive the current global model weights \(w_{t}\) from the central server. K selected clients then train the model locally using its data \(D_{k}\) for E epochs, and update local weights \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Characteristic** & **Massive IoT (MIoT)** & **Industrial IoT (IIoT)** & **Artificial Intelligence of Things (AIoT)** & **General Consumer IoT** \\ \hline Network Topology and Connectivity & LPWAN (LoRaWAN, NB-IoT, Sigfox), cellular IoT; star or mesh topologies; focus on long range, low power & Wired (Industrial Ethernet, Profibus), re-liable wireless (e.g., private 5G, Wi-Fi HaLow); often deterministic networks; Focus on reliability, low latency & Diverse: Wi-Fi, 5G/6, Bluetooth, Zigbee, wired; edge-centric or device-to-device communication; focus on bandwidth and latency for AI processing & Wi-Fi, Bluetooth, Zigbee, Z-Wave; typically star or mesh connected to a home hub/router; focus on ease of use and interoperability \\ \hline Key Constraints and Challenges & Extreme low power, low cost per device, massive scalability, simple device management, intermittent connectivity & Ultra-high reliability, low and deterministic latency, security against cyber-physical attacks, harsh operating environments, interoperability of legacy systems [31] & Computational power for AI on device/edge, energy for AI processing, real-time AI inference, complexity of AI model deployment and management, data quality for AI & User privacy, security vulnerabilities, ease of setup and use, inter-operability between vendor ecosystems, device cost \\ \hline LLM/FL Relevance & FL for anomaly detection across massive datasets, simple staus summarization by LLMs (if data aggregated); SFL for very basic feature extraction. & FL for predictive maintenance models, LLMs for analyzing maintenance logs and generating reports, LLMs for human-machine interfaces (NL queries about machine status). & FL for training sophisticated AI models (e.g., vision, speech) at the edge, LLMs for complex scene understanding, natural language interaction, and autonomous decision-making. & FL for personalized models (e.g., smart home routines), LLMs for voice assistants and intuitive control; SFL for privacy-preserving on-device learning [32] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._\(w_{t+1}^{k},k\in K\). The server aggregates these local weights to produce the updated global model \(w_{t+1}\), as defined in (1): \[w_{t+1}\leftarrow\sum_{k=1}^{K}\frac{n_{k}}{n}w_{t+1}^{k},\] (1) where \(n_{k}=|D_{k}|\) is the number of data points on client k, and \(n=\sum_{k=1}^{K}n_{i}\) is the total number of data points across the selected clients [40]. This weighted average aims to give more importance to updates from clients with more data. The adoption of FL, particularly in sensitive or distributed environments like IoT, is driven by several key advantages over traditional centralized approaches [7]: * Enhanced Privacy: Data remains localized on user devices, reducing risks associated with central data aggregation. * Communication Efficiency: Transmitting model updates instead of raw data significantly reduces network load. * Utilizing Distributed Resources: Leverages the computational power available at the edge devices [41]. While FedAvg provides a foundational approach, practical FL implementations involve several key characteristics, architectural choices, and challenges: * CFL vs. DFL: Centralized FL (CFL) uses a server for coordination and aggregation, offering simplicity but creating a potential bottleneck and single point of failure [42].