Federated Learning Approach for Remote Sensing Scene Classification

Abstract

In classical machine learning algorithms, used in many analysis tasks, the data are centralized for training. That is, both the model and the data are housed within one device. Federated learning (FL), on the other hand, is a machine learning technique that breaks away from this traditional paradigm by allowing multiple devices to collaboratively train a model without each sharing their own data. In a typical FL setting, each device has a local dataset and trains a local model on that dataset. The local models are next aggregated at a central server to produce a global model. The global model is then distributed back to the devices, which update their local models accordingly. This process is repeated until the global model converges. In this article, a FL approach is applied for remote sensing scene classification for the first time. The adopted approach uses three different RS datasets while employing two types of CNN models and two types of Vision Transformer models, namely: EfficientNet-B1, EfficientNet-B3, ViT-Tiny, and ViT-Base. We compare the performance of FL in each model in terms of overall accuracy and undertake additional experiments to assess their robustness when faced with scenarios of dropped clients. Our classification results on test data show that the two considered Transformer models outperform the two models from the CNN family. Furthermore, employing FL with ViT-Base yields the highest accuracy levels even when the number of dropped clients is significant, indicating its high robustness. These promising results point to the notion that FL can be successfully used with ViT models in the classification of RS scenes, whereas CNN models may suffer from overfitting problems.