## 5 Experiments and Results
### Application of FL P2P for DFU Classification  
The overall architecture we are proposing for the classification of DFU is based on the Siamese network. The Siamese network was presented in the context of signature verification [18] and comprises two identical networks that take in separate inputs but are connected in the last layer.  
Figure 4 gives a high-level view of the Siamese network as a block diagram. The Siamese neural network usually uses contrastive loss [33], which aims to maximize the proximity between positive pairs while simultaneously increasing the dissimilarity between negative pairs.  
For the CNN backbone, we used EfficientNetV2S based on EfficientNet [34] architectures, which have been shown to significantly outperform other networks in classification tasks while having fewer parameters. EfficientNetV2S has fewer parameters, making it more suitable for low-resource settings, and it uses a combination of efficient network design and compound scaling to achieve high accuracy with fewer parameters [35]. The second backbone of the ensemble model is based on Vision Transformers. This was first introduced by the paper \"An Image is Worth 16 \(\times\) 16 Words: Transformers for Image Recognition at Scale\" [36] and is referred to as Vision Transformers (ViTs).  
The classification model is a milestone in the development of an innovative tool to be used to assist medical health professionals in performing follow-ups of patient with DFUs. Figure 5 illustrates the proposed approach to DFU classification.  
Figure 4: Block diagram of Siamese network.  
Figure 5: Overall schematics of the innovative framework for DFU classification [17].  
The ensemble model architecture we propose to experiment with uses EfficientNetV2S as the CNN backbone and Bidirectional Encoder representation from Image Transformers (BEiTs) for the Vision Transformer as proposed by [PERSON] et al. [17]. Figure 6 shows the internal architecture of the ensemble model.  
In deep learning, the efficacy of a model is highly dependent on the quality and representativeness of its training data. It is imperative to tackle data bias to ensure robust and accurate model performance. This challenge is particularly pronounced in our multi-class classification of diabetes foot ulcers (DFUs), where there may be an uneven representation of classes. As detailed in Section 5.3, the data distribution across classes--both infection, ischemia, and none--is 621, 2555, 227, and 2552, respectively. Notably, the ischemia class is underrepresented with only 227 instances, despite including augmented images.  
Constructing a balanced dataset would ideally involve collaboration among various medical centers to share DFU images. While the benefits of artificial intelligence in medical diagnostics are widely recognized, the sharing of medical data raises substantial ethical concerns. To this end, federated learning presents a promising solution. It aims to augment the training dataset while upholding the confidentiality of sensitive medical data by retaining them at the source. This approach not only enhances the model's training but also addresses privacy and ethical considerations inherent in medical data handling.