# Few-Shot Federated Learning: A Federated Learning Model for Small-Sample Scenarios
## 4 Model System
### Model Learning Process
FedAvg: In contrast, the performance of the classic federated learning algorithm under these conditions is barely satisfactory, which further proves that traditional federated learning struggles to find effective applications in small-sample scenarios. Additionally, the effectiveness of our approach on the FC100 and MiniImageNet datasets is also related to the neural network used for extracting image features. FedFSL: Only on the MiniImageNet dataset, the method proposed in this paper performs better in the one-shot scenario, but the F1 score is lower than the method proposed in this paper on all three datasets, further demonstrating the effectiveness of the method proposed in this paper. ## 6 Conclusions

In this paper, the challenges of applying federated learning in few-shot scenarios are explored, and a new model, FsFL, is introduced. This model builds on federated learning by integrating few-shot learning techniques. A personalized knowledge distillation approach has been devised that enables client models to partially distill knowledge from server-side models, thereby enhancing the robustness of federated learning. This allows it to complete model training and ultimately converge in scenarios with extremely limited data, showing considerable classification accuracy in few-shot scenarios. This model was tested on three image datasets, and the results indicate that in one-shot scenarios, the performance improvement exceeded 100%. In five-shot scenarios, there was also a significant enhancement, for instance, a more than 70% improvement on the MiniImageNet dataset, and over 30% on the other two datasets. At the same time, the model accuracy and F1 score obtained by this method are also better than advanced FedFSL schemes. These results highlight the superiority of our proposed method in scenarios with small data samples; at the same time, it also reflects the value of the model in practical scenarios. Conceptualization, X.C. ; Methodology X.C. ; Software X.C. ; Validation X.C. ; Formal Analysis X.C. ; writing--original draft preparation, X.C. ; Writing--Review and Editing, J.T. ; Funding Acquisition, J.T. ; Supervision, S.W. All authors have read and agreed to the published version of the manuscript. This research was funded by Natural Science Foundation of Hebei Province, grant number F2021201049, Natural Science Fund Committee of Hebei Province (F2021201058) and Funds of the Central Government for Local Science and Technology Development (236Z0701G). Not applicable. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Omniglot** & **FC100** & **MiniImageNet** \\ \hline FsFL & 0.907 & 0.603 & 0.712 \\ \hline Fedavg & 0.685 & 0.434 & 0.394 \\ \hline FedFSL & 0.875 & 0.593 & 0.632 \\ \hline \end{tabular}
\end{table} 
Table 2: F1-score. Data Availability Statement: All of datasets used in this experiment are publicly available datasets at the following URLs: MinImageNet. [[https://www.kaggle.com/datasets/arjunashok33/minimagenet/download?datasetVersionNumber=1](https://www.kaggle.com/datasets/arjunashok33/minimagenet/download?datasetVersionNumber=1)]([https://www.kaggle.com/datasets/arjunashok33/minimagenet/download?datasetVersionNumber=1](https://www.kaggle.com/datasets/arjunashok33/minimagenet/download?datasetVersionNumber=1)) (accessed on 29 April 2024); FC100. [[https://github.com/learnables/learn2](https://github.com/learnables/learn2) learn/blob/master/learn2 learn/vision/datasets/fc100.py]([https://github.com/learnables/learn2](https://github.com/learnables/learn2) learn/blob/master/learn2 learn/vision/datasets/fc100.py) (accessed on 29 April 2024); Omniglot. [[https://www.kaggle.com/datasets/watesoyan/ommiglot](https://www.kaggle.com/datasets/watesoyan/ommiglot)]([https://www.kaggle.com/datasets/watesoyan/ommiglot](https://www.kaggle.com/datasets/watesoyan/ommiglot)) (accessed on 29 April 2024). The authors declare that this study was conducted without any commercial or financial relationships that could be considered potential conflicts of interest.