## 3 Materials and Methods
### Model  
We utilized the Eff-AQI model [50] from our previous study for this research. Initially, its purpose was to rapidly calculate PM\({}_{10}\), PM\({}_{2.5}\), and air quality index (AQI) values from image inputs. Our commitment to advancing AI for social good and SDG 17 served as the driving force behind the model development. Also, it was developed as part of the national project known as Integrated Command and Control Center for a Sustainable Smart City.  
The efficacy, suitability for deployment on edge GPUs, and compatibility with the study's goals were the deciding factors in choosing this model. In order to reduce the size of the model and alter its function from value estimation to AQI classification, specific model layers were modified. The overall model size was reduced without affecting the level of accuracy that was considered acceptable. The decrease in model size had positive effects in the FL environment, as it led to reduced communication costs throughout each round of the FL aggregation procedure.  
It is important to acknowledge that although our model selection was based on these considerations, it is worth noting that other established models like MobileNet, ResNet, VGG16, and others could be employed. However, hardware specifications may differ as a result of model architectures and size variations.  
The adapted model architecture employed in this study was presented in Figure 5. Specifically designed for processing RGB images with dimensions of 180 \(\times\) 180 \(\times\) 3, the model comprised distinct blocks and layers for effective feature extraction and classification. The initial model block encompassed two convolutional neural network (CNN) layers, each featuring 64 filters, along with a max-pooling layer. Subsequently, the second model block consisted of two CNN layers, each incorporating 128 filters, and a single max-pooling layer.  
Notably, the modified residual blocks in the third through fifth stages consisted of a singular CNN layer. The output of this CNN layer was added to the preceding max-pooling output before being forwarded to the subsequent max-pooling layer. The first three max-pooling layers employed a 3 \(\times\) 3 kernel and stride size, while the fourth and fifth layers used a 2 \(\times\) 2 kernel and stride size. The leaky rectified linear unit (LeakyReLU) was adopted as the activation function for all CNN layers, replacing the traditional rectified linear unit (ReLU). This decision was motivated by the ability of LeakyReLU to address the issue of ReLU termination [53] and its demonstrated effectiveness in enhancing model accuracy [54].  
The five blocks dedicated to feature extraction were followed by a flattening process, and the resultant features were conveyed to sets of fully connected layers. The initial fully connected layer housed 256 neurons, followed by a layer with 512 neurons. Both fully connected layers utilized LeakyReLU as their activation function. The final, fully connected layer, encompassing six neurons, employed the softmax activation function for AQI classification.  
A deliberate choice was made to incorporate residual blocks, which are known to mitigate gradient disappearance issues and enhance feature extraction efficacy [55; 56]. To manage computational complexity during the initial stages of the extraction process, residual blocks were excluded from the first two blocks, where a reduced number of CNN filters was deemed sufficient. The overall model comprised 870,598 parameters, which were equivalent to 3.54 MB. The adoption of LeakyReLU and residual blocks aligned with the intention to optimize model performance in feature extraction and classification tasks.