Federated Learning for COVID-19 Detection: Optimized Ensemble Weighting and Knowledge Distillation

The COVID-19 pandemic has underscored the need for effective diagnostic tools, particularly in resource-limited settings. While RT-PCR and CT scans are standard, their limitations drive the need for advanced techniques. This study leverages Convolutional Neural Networks, Knowledge Distillation, Ensemble Learning, and Federated Learning to develop robust, privacy-preserving models for COVID-19 detection from CT scans. We propose two federated learning strategies to simplify deep learning models for use in clinical environments with limited computational resources. The first strategy uses knowledge distillation from a complex model to a simplified model shared across a federated network. The second allows each hospital to distill knowledge to its simplified model, later combined into a global model via ensemble learning. Our methods, AFKD and IKDEFL, outperform traditional federated learning approaches such as FedAvg and FedAdam. AFKD, paired with the COVID-CNN model, achieves 91%-95% accuracy on IID (Independent and Identically Distributed) datasets and 70%-89% on non-IID datasets. IKDEFL further improves performance, with 92%-95% accuracy on IID datasets and 76%-88% on non-IID datasets. These approaches provide promising solutions for enhancing COVID-19 detection in federated learning.