The proposed Digital Zero-Order Federated Learning (DZOFL) method can be extended to handle heterogeneous data distributions by incorporating techniques that account for the variability in data quality and quantity across different devices. One approach is to implement a weighted aggregation mechanism where the contribution of each device's scalar update is weighted based on the amount of data it holds or the quality of its local model. This can be achieved by modifying the aggregation step in the DZOFL algorithm to include weights that reflect the importance of each device's contribution, thus ensuring that devices with more representative or larger datasets have a greater influence on the global model update.
Additionally, the DZOFL method can be adapted to include personalized models for each device. This involves allowing devices to maintain local models that are fine-tuned based on their specific data distributions while still participating in the global training process. Techniques such as meta-learning or multi-task learning can be integrated into the DZOFL framework to facilitate this personalization, enabling the model to learn shared representations while also adapting to the unique characteristics of each device's data.
Moreover, the algorithm can be enhanced by incorporating strategies for data augmentation or synthetic data generation to balance the datasets across devices. This would help mitigate the effects of data heterogeneity and improve the overall performance of the federated learning system.