Research has been conducted to reduce resource consumption in 3D medical image segmentation for diverse resource-constrained environments. However, decreasing the number of parameters to enhance computational efficiency can also lead to performance degradation. Moreover, these methods face challenges in balancing global and local features, increasing the risk of errors in multi-scale segmentation. This issue is particularly pronounced when segmenting small and complex structures within the human body. To address this problem, we propose a multi-stage hierarchical architecture composed of a detector and a segmentor. The detector extracts regions of interest (ROIs) in a 3D image, while the segmentor performs segmentation in the extracted ROI. Removing unnecessary areas in the detector allows the segmentation to be performed on a more compact input. The segmentor is designed with multiple stages, where each stage utilizes different input sizes. It implements a stage-skipping mechanism that deactivates certain stages using the initial input size. This approach minimizes unnecessary computations on segmenting the essential regions to reduce computational overhead. The proposed framework preserves segmentation performance while reducing resource consumption, enabling segmentation even in resource-constrained environments.

Federated Learning (FL), a practical solution that leverages distributed data across devices without the need for centralized data storage, which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing. Despite its privacy-preserving advantages, FL remains vulnerable to backdoor attacks, where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process. While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL, they often incur a significant degradation in the performance of the aggregated models on benign tasks. To address this limitation, we propose a novel backdoor defense mechanism based on differential privacy. Our approach first utilizes the inherent out-of-distribution characteristics of backdoor samples to identify and exclude malicious model updates that significantly deviate from benign models. By filtering out models that are clearly backdoor-infected before applying differential privacy, our method reduces the required noise level for differential privacy, thereby enhancing model robustness while preserving performance. Experimental evaluations on the CIFAR10 and FEMNIST datasets demonstrate that our method effectively limits the backdoor accuracy to below 15% across various backdoor scenarios while maintaining high main task accuracy.