# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 4 Mobility-Aware Client Selection (MACS) Algorithm  
MACS applies a heuristic approach to maximize the number of selected clients while ensuring they meet the latency requirements: (1) All clients are initialized as unselected (\(y_{u}=0\)). The set of selected clients (\(\mathcal{Q}\)) is initially empty, and the iteration counter \(t\) is set to zero. (2) Each client is evaluated based on its predicted latency, and scores are assigned accordingly. Clients with a positive score are ranked in descending order of \(S_{u}\). (3) The top \(N\) clients (or those with \(S_{u}>0\)) are selected to participate. During each iteration, clients are reassessed, and the resource allocation is adjusted to ensure the feasibility of participation. (4) A dynamic resource allocation strategy is employed, which ensures that the total allocated bandwidth for clients does not exceed the available bandwidth of the base station. The MACS algorithm iteratively updates client selection, ensuring the global model benefits from diverse and stable contributions while addressing both resource and mobility constraints. The MACS algorithm is summarized in Algorithm 1.  
The MACS algorithm is composed of three core components:  
1. Mobility Prediction Model: This module forecasts the future mobility states of clients based on historical data and trajectory patterns. It determines the likelihood of a client maintaining stable connectivity during training, enabling informed client selection.
2. Resource-Aware Client Evaluation: Clients are evaluated based on computational capacity, data rate, and predicted mobility states. This ensures that selected clients can contribute effectively without causing delays.  
Dynamic Resource Allocation: Real-time feedback from participating clients is used to adjust computational resources and communication bandwidth dynamically. This adaptive allocation optimizes resource utilization while minimizing training delays.  
```
1 Initialization: Initialize selected client set \(Q=\emptyset\).
2 forach client \(u\) in \(U\)do
3 Predict future CPU frequency \(f_{u}(t+k)=\text{PredictCPUFrequency}(\hat{M}_{u}(t+k))\);
4 Predict future data rate \(r_{u}(t+k)=\text{PredictDataRate}(\hat{M}_{u}(t+k))\);
5 Compute computational latency: \(t_{\text{comp},u}(t+k)=\frac{C_{u}\times|D_{u}|\times\text{p}\times\text{log} _{2}(1/\eta)}{f_{u}(t+k)}\);
6 Compute upload latency: \(t_{\text{upload},u}(t+k)=\frac{s}{r_{u}(t+k)}\);
7 Compute total predicted latency: \(t_{\text{total},u}(t+k)=t_{\text{comp},u}(t+k)+t_{\text{upload},u}(t+k)\);
8 if\(t_{\text{total},u}(t+k)\leq\tau\)then
9 Assign score \(S_{u}=\frac{1}{t_{\text{total},u}(t+k)}\);
10 end if
11 else
12\(S_{u}=0\); // Client is not eligible due to exceeding deadline
13 end if
14
15 end for
16 Rank clients \(u\) in \(U\) based on their scores \(S_{u}\) in descending order.
17 fortop\(N\) clients (or clients with \(S_{u}>0\))do
18 Add client \(u\) to \(J\);
19
20 end for
21 Broadcast global model to selected clients \(Q\).
22 Clients in \(Q\) perform local training and upload their updates.
23 Aggregate updates at the server to update the global model.
24 forach client \(u\) in \(U\)do
25 Update \(\hat{M}_{u}(t+k)\) based on observed network conditions and computation times.
26
27 end for
28 Repeat until model convergence or training objective is met.
```  
**Algorithm 1**Mobility-Aware Client Selection (MACS)  
The MACS strategy addresses the challenges of federated learning (FL) in dynamic IoT environments characterized by client mobility and resource heterogeneity. By incorporating a mobility prediction model, resource-aware evaluation, and dynamic resource allocation, MACS optimizes the client selection process to ensure efficient training under stringent latency constraints. By dynamically predicting client states, MACS selects clients likely to maintain stable communication and computational capabilities throughout a training iteration. This approach enhances convergence rates and improves the overall efficiency of resource utilization in federated learning.