## 3 Proposed FP-SFOA-DQN-FL for Real-World Object Recognition in FL
### Object Detection Using SegNet  
The pre-processed outcome \(B_{a}\) is injected into the object-detection stage, where objects are accurately detected by employing SegNet. It is specifically developed to be an effective structure for pixel-wise semantic segmentation. The spatial differences between diverse classes are very comprehensible. Also, the SegNet used few trainable parameters, so it is more efficient in terms of computation time, accuracy, and memory. 1. Structural diagram of SegNet  
SegNet [28] consists of an encoder and decoder network, succeeding with a pixel-wise classification layer. The encoder includes 13 convolutional layers, which are particularly designed for object classification. The training process will be initialized from weights tuned for categorization on huge databases. The fully connected layers are eliminated to extract the higher-resolution feature maps, and it also minimizes the quantity of SegNet parameters. Each encoder has its respective decoder, so the decoder network consists of 13 layers. Finally, the decoder result is subjected to a softmax classifier to generate class probabilities for individual pixels. The layers in the SegNet are elaborated as follows:  
Figure 2: Pictorial illustration of object-recognition process performed in the training model. 1. Encoder network  
This network applies convolution operation with a filter bank to generate a pool of feature maps. After that, the feature maps undergo a batch-normalized operation, and an element-wise function is performed utilizing a Rectified Linear Unit (ReLU). Following this, a max-pooling operation and non-overlapping window are carried out, and the final outcome is sub-sampled utilizing a parameter of 2. Sub-sampling generates a huge input image context within the feature map. However, numerous max-pooling and sub-sampling layers result in high translation invariance for effective classification. This lossy image illustration is not suitable for the segmentation process. In order to address this gap, it is imperative to grasp and preserve the boundary data in the encoder feature maps. 2. Decoder network  
In this network, it up-samples its feature maps employing the retained max-pooling indices from respective encoder feature maps. During this phase, sparse feature maps are generated. Such feature maps are again convolved with a trained decoder filter bank to generate dense feature maps. Then, the feature maps are processed using a batch-normalization step. It is notable that the decoder, with respect to its first encoder, generates a multi-channel feature map, even if the encoder input has three channels. 3. Softmax classifier  
The result of the final decoder, which is a high-dimension feature representation, is subjected as an input to the trainable soft-max classifier. The outcome of the soft-max classifier is a \(K\) channel image of probabilities, which specifies the count of classes. The detected result obtained through SegNet is denoted as \(D_{a}\). Figure 3 portrays the structural diagram of SegNet. 2. Training of SegNet using proposed PSFOA  
In order to attain an accurate detected object result, it is significant to train the SegNet with an efficient hybrid optimization at every epoch until the satisfied result is obtained. Here, a hybrid algorithm named PSFOA is employed, which is designed by the combination of PO with SFOA. SFOA [29] is inspired by the immature sunflowers that generate heliotropic movements. Here, two growth strategies are managed on the heliotropic motions of baby sunflowers. The initial process is the sun-tracking occurrence, which is influenced by a growth hormone known as Auxin. On the other hand, the second strategy is the biological clock. Moreover, this technique has been introduced in two stages, namely, sunny and rainy or cloudy phases. PO [30] is a socially inspired metaheuristic algorithm  
Figure 3: Structural diagram of SegNet. that is inspired by the multi-phased process of politics.