# Few-Shot Federated Learning: A Federated Learning Model for Small-Sample Scenarios
## 1 Introduction  
In the current realm of machine learning and deep learning, federated learning has emerged as a highly focused research direction, offering the potential to construct global models in decentralized data environments. Federated learning allows multiple participants to train models in a distributed manner without centralizing data storage in a single server, thus preserving data privacy and security. However, while current federated learning frameworks excel with large-scale, high-quality datasets, their performance significantly diminishes with small-scale, few-shot datasets [1].  
In practical applications [2; 3; 4], training processes often encounter situations where participants may possess a limited amount of data or are exploring an emerging field with scarce available data. This underscores the imperative need for further investigations on how to ensure adequate robustness in joint learning under scenarios with limited data samples, thereby facilitating efficient model training without compromising the availability of the global model. Addressing this issue is critical for the extensive deployment of federated learning.  
In response to the challenges faced by traditional federated learning in training global models under few-shot data scenarios, this chapter introduces a novel federated learning model: the Few-Shot Federated Learning model (hereinafter referred to as FsFL). Thismodel incorporates few-shot learning [5] to overcome the limitations of traditional federated learning in few-shot scenarios. By integrating few-shot learning techniques into the federated learning model training process, the model aims to enable learning and generalization from only a small number of samples. Integrating few-shot learning techniques into the federated learning system reduces the dependency of traditional federated learning model training on large-scale datasets, achieving the objective of model training in few-shot data scenarios. This allows for effective model training progression even with extremely limited data. Simulation experiments have demonstrated the enhanced robustness of the federated learning system in few-shot data scenarios, expanding the applicability of federated learning.  
Our contributions:  
1. Overcoming the Challenges of Training Models with Extremely Limited Data: In practical applications, acquiring a large volume of labeled data can be prohibitively expensive. A few-shot federated learning model has been designed that enables the normal progression of model training even in scenarios with limited data availability.
2. Enhancing Privacy Protection: In an era where privacy and security issues are of paramount concern, ensuring that user privacy is not compromised during model training is crucial. Therefore, federated learning has been adopted as the foundational framework. This framework avoids the privacy leaks that could occur through the sharing of client's original data.
3. Facilitating Personalized Learning on the Client Side: Considering real-world scenarios, especially those with extremely limited data, where data distributions are often non-independent and identically distributed (non-IID), and participants' classification tasks can vary greatly, personalized knowledge distillation has been introduced. This enables clients to perform partial knowledge distillation to the server, thereby further improving the training efficiency of the model.