# Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
## 4 Experiments
### Analysis of Results and Future Research  
Through extensive experiments, we have validated the effectiveness of the PFPS-LWC approach in personalized federated learning, particularly in addressing critical challenges such as data heterogeneity and fluctuations in client participation rates. We conducted experiments on the cifar10 and cifar100 datasets, using various data partitioning methods, including Dirichlet and pathological Non-IID partitioning. The results show that PFPS-LWC performs exceptionally well across different environmental settings, significantly outperforming existing methods, thus demonstrating the robustness of our approach.  
In scenarios with varying data distributions and participation rates, PFPS-LWC exhibits greater adaptability compared to traditional federated learning methods. By employing Progressive Local Training Strategies and lightweight classifiers, PFPS-LWC effectively enhances the client's local model performance and significantly improves the accuracy and stability of the global model. Moreover, in environments with limited computational resources and high data heterogeneity, PFPS-LWC shows notable advantages, demonstrating its strong robustness and generalization capability.  
Despite its strong performance, PFPS-LWC has some limitations. In scenarios with extremely high data heterogeneity, there is still potential for further performance improvements. Future work could focus on refining the knowledge recall mechanism to enhance the model's adaptability and stability in such challenging environments. Additionally, while PFPS-LWC has been evaluated on visual datasets like cifar10 and cifar100, its applicability could be extended to more complex, domain-specific datasets, such as medical or IoT data, to assess its broader effectiveness.  
Regarding changes in the model architecture, we recognize that different network architectures can impact both the results and conclusions of the model. The five-layer CNN architecture we are currently using performed well in our experiments and is widely applied in related research. However, with changes in the network architecture (such as adding or removing layers or altering the number of neurons per layer), the model's performance and the convergence speed of the global model may vary. Altering the architecture could lead to improvements or declines in performance, depending on the model's complexity, the nature of the training data, and computational resource limitations. Theoretically, however, the PFPS-LWC method is a systematic approach, and its performance is less affected by structural changes. Therefore, future research could explore the impact of different architectures on the effectiveness of PFPS-LWC and further optimize the architectural design to enhance performance across different data distributions and application scenarios.  
Future research can focus on several key areas: first, further optimizing the knowledge recall mechanism to address challenges posed by extreme data heterogeneity; second, enhancing the computational efficiency of the model, particularly for edge computing and IoT devices, to reduce computational and storage burdens; and finally, exploring the potential of PFPS-LWC in cross-domain applications (such as healthcare, autonomous driving, etc.), with a focus on data privacy protection and real-time adaptability.