# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 4 Proposed Approach
### Federated Learning with Data Sharing and Pre-Training on Augmented Data
This increased diversity in the training data is critical for developing a robust model capable of generalizing to unseen data, which is particularly crucial in federated learning environments with non-IID data across clients. * **Pre-Training of the Global Model:** After augmentation, the global model is pre-trained on the set of 50,000 augmented images. The global model is the CNN model defined for the CIFAR-10 dataset in Section 3.2, which achieves an evaluation accuracy of approximately 70% and an evaluation loss of 0.92 upon completion of pre-training. This pre-training aims to establish a strong basis for performance and accelerate the process of model convergence during federal learning. * **Federated Learning:** Finally, after pre-training, the global model is now ready to proceed to federated training with the important difference that it is no longer initialized with random weights. Instead, it loads weights from pre-training, providing a stable starting point based on previous training with the augmented dataset. This approach ensures that the model has already developed a strong understanding of the various features and patterns presented in the augmented images, increasing the chances of more efficient and faster convergence during federated learning. The federated training follows the steps of the FedAvg algorithm described in Section 4.1, while also incorporating the Cyclical Learning Rate (CLR) policy detailed in Section 4.2. In federated learning environments with non-IID data, each client may only have access to a limited and skewed subset of the overall data distribution. This can lead to models that overfit client-specific data and fail to generalize well across the global dataset. Pre-training on a balanced and augmented dataset provides the model with a strong foundation by exposing it to a more comprehensive set of data features. As a result, the global model can better capture the underlying data structure and is less affected by the variations in local client datasets. By starting with pre-trained weights, the model converges faster and with greater stability during the federated learning process, particularly in non-IID environments, where initial random weight initializations would otherwise lead to slower and less reliable training. Based on the need to improve federated learning in non-IID data scenarios, the proposed strategy combines pre-training on augmented data and sharing a balanced subset of data among all clients. Additionally, the integration of the Cyclical Learning Rate (CLR)  
Figure 1: Example of application of the above augmentation techniques to a random CIFAR-10 image. technique offers further optimization through cyclical adjustments of the learning rate, enhancing the model's generalization and convergence speed. The flexibility in adjusting the size of the balanced subset and the sharing rate tailors the strategy to specific problem needs and applications. This strategy requires execution only once before federated learning begins, thus communication cost is not a barrier. Moreover, the shared data constitutes a separate set from each client's private data, ensuring no privacy concerns. Overall, this strategy enhances the model's ability to recognize patterns and features across a wide range of conditions, improving accuracy and generalization in non-IID data environments. The general framework of the proposed methodology, including the pre-training, data sharing, and CLR optimization strategies, is demonstrated in Figure 2.