# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 3 System Models and Problem Formulation  
To address the challenges posed by client mobility and resource variability in federated learning (FL), this paper proposes the Mobility-Aware Client Selection (MACS) strategy. MACS ensures that selected clients maintain stable computational and communication capabilities while meeting latency constraints, improving the overall training process. Consider a federated learning system consisting of a base station (BS) that orchestrates the FL process and a set of distributed IoT clients, denoted as \(\mathcal{U}\), located within the BS's coverage area. Each client \(u\in\mathcal{U}\) can either participate in the FL training process (\(y_{u}=1\)) or not (\(y_{u}=0\)), where \(y_{u}\) is a binary variable indicating client selection. MACS evaluates all clients in \(\mathcal{U}\) based on their predicted mobility, computational capacity, and communication quality, selecting a subset of clients \(\mathcal{Q}\subseteq\mathcal{U}\) for each training round.  
Figure 1 illustrates the reference scenario of federated learning in a dynamic IoT environment. At time \(t\), mobile clients such as smartphones and sensors participate in local training and send model updates to the FL server through a base station. As clients move (e.g., Client 2), connectivity may fluctuate, affecting their ability to contribute updates. By time \((t+k)\), client positions change, introducing challenges such as unstable participation, delayed transmissions, and potential connectivity loss. The FL server aggregates the received updates and broadcasts the global model for the next round. MACS addresses these challenges by predicting client mobility and selecting stable clients based on communication quality and computational resources, ensuring reliable participation and minimizing training delays.  
\begin{table}
\begin{tabular}{p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt}} \hline \hline
**Approach** & **Mobility Handling** & **Client Selection** & \begin{tabular}{c} **Computational** \\ **Efficiency** \\ \end{tabular} & **Limitations** \\ \hline RL-based FL [13, 31, 32] & Partial & \begin{tabular}{c} Reinforcement \\ learning \\ \end{tabular} & High & \begin{tabular}{c} Computationally \\ intensive, making it \\ impractical for \\ low-resource IoT \\ devices. \\ \end{tabular} \\ \hline MACS (Proposed) & Yes (predictive) & \
\begin{tabular}{c} Mobility-aware, \\ resource-adaptive \\ \end{tabular} & Low &
\begin{tabular}{c} Requires mobility \\ estimation but \\ improves long-term \\ client stability and \\ training efficiency. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Unlike Static or Random selection strategies, MACS improves client selection in FL through three key mechanisms. First, the mobility prediction component estimates whether a client will remain connected for the entire FL round based on historical movement data and real-time conditions. Second, the resource-aware selection mechanism filters clients based on computational power and network stability, preventing delays and failed updates. Finally, dynamic adaptation continuously adjusts client selection strategies to reduce dropout rates and ensure timely training completion. These mechanisms enable MACS to operate efficiently in IoT networks with high mobility and diverse resource constraints, improving convergence speed, reducing communication overhead, and enhancing the robustness of federated learning in dynamic environments.