# Few-Shot Federated Learning: A Federated Learning Model for Small-Sample Scenarios
## 2 Related work  
### Few-Shot Learning  
Few-shot learning and federated learning are paradigms in machine learning where the goal is to acquire \"knowledge\" from a very limited number of samples and use this \"knowledge\" to achieve a certain objective. Few-shot learning can be further divided into few-shot classification [6; 7], few-shot regression [8], and few-shot detection [9; 10]. The methods involved in few-shot classification include metric learning methods, model fine-tuning methods, and meta-learning methods. The core idea of metric learning methods is categorizing samples based on the computation of distances or similarities between them, enabling the identification of unknown samples by their distance or similarity to known categories. Key approaches include the following:  
1. Siamese Networks: [PERSON] et al. [11] first introduced the concept of Siamese networks in their paper, employing two identical networks to learn the similarity between input pairs. These networks share the same weights and architecture and are trained jointly to minimize or maximize some distance metric, thereby determining whether two inputs belong to the same category. [PERSON] et al. [12] proposed a method based on Siamese networks for building text classifiers, embedding texts and labels into a common vector space, and using a similarity function to calculate the similarity between two items. [PERSON] et al. [13] innovated on the Siamese network architecture by introducing the Stop Loss function to prevent representation collapse, simplifying the training process and enhancing model robustness. 2. Matching Networks: [PERSON] et al. [14] introduced matching networks, incorporating attention mechanisms and memory modules to enable the model to learn a matching function for a small-sample task directly from the support set. The design of matching networks allows the model to consider all samples in the support set at each step, learning how to make effective predictions from a few samples in an end-to-end manner. [PERSON] et al. [15] proposed a Bi-directional Matching Network architecture,incorporating a semantic alignment model and combining appearance flow, relation flow, and mutual information flow for sample alignment and comparison. This method addresses the challenge of image classification in few-shot settings by leveraging the deep semantic relationships between images, significantly improving the classification performance. [PERSON] et al. [16] presented SGMNet, a meta-learning framework based on scene graph matching for few-shot remote sensing scene classification, introducing a Graph Construction Module (GCM) and a Graph Matching Module (GMM) to effectively utilize the co-occurrence and spatial correlation features of remote sensing images, enhancing the classification performance. 3. Prototypical Networks: [PERSON] et al. [5] introduced Prototypical Networks, which classify by computing prototypes for each category--the averages of all sample features within a class--and then classifying query samples based on their distance to these prototypes. The essence of Prototypical Networks is to represent each category in the feature space such that the features of samples within the same category are as close together as possible, while those of different categories are far apart. This approach has shown excellent performance in few-shot learning tasks, especially in one-shot and few-shot learning scenarios, effectively improving model accuracy and the capacity for generalization. [PERSON] et al. [17] proposed a new architecture based on Prototypical Networks, LDP-Net, employing a dual-branch network structure to predict the category of an input image using a global branch method. The image is then randomly cropped and the cropped image is used as a new input for prediction. After prediction, knowledge distillation is used to reinforce the consistency between the overall and partial predictions of the image, thereby enhancing the model's generalization performance. [PERSON] et al.