Federated deep convolutional neural network architecture for renal cancer classification

Abstract

Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults, its early detection and clinical diagnosis are crucial for reducing mortality. Deep convolutional neural networks (DCNNs) have demonstrated outstanding performance in medical image classification. Therefore, we selected the pre-trained EfficientNet-B2 as the backbone architecture for its lightweight and efficient characteristics. However, due to the implementation of privacy protection policies, acquiring sensitive medical data such as those related to kidney cancer has become increasingly challenging. Federated learning (FL) safeguards patient privacy by sharing only model parameters instead of raw data. Nevertheless, medical image data often exhibits non-independent and identically distributed (non-IID) characteristics, data sparsity, and imbalanced distributions, which adversely affect model performance. To address these issues, we propose an optimized federated learning framework, FedMilNet, which evaluates the data quality of each client and then utilizes dynamic curriculum learning. This approach arranges the sequence of client participation from easy to difficult based on data quality, gradually expanding the candidate frontier. Additionally, we determine “commanders” and “auxiliary teachers” based on the quality score, dynamically adjusting their weights as training progresses. FedMilNet achieved an accuracy of 62.71% on the ccRCC dataset, outperforming mainstream federated baselines by approximately 10%. On the Kaggle Kidney Cancer and CT KIDNEY DATASET, it reached high accuracies of 99.83% and 95.78%. By quantifying and utilizing client data quality in federated environments, and integrating curriculum-based client selection with dynamic knowledge distillation, our approach significantly enhances model generalization and accuracy under small-sample and non-IID conditions, advancing scientific progress in privacy-preserving and practical intelligent diagnosis of kidney cancer.

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