Brain tumors account for approximately 2.5% of cancer‐related deaths. Accurate classification of brain tumor types is essential for timely diagnosis and enhancing survival rates. Convolutional neural networks (CNNs) have demonstrated state‐of‐the‐art performance in computer‐aided diagnosis of brain tumors; however, the quality and availability of medical data significantly influence this process. Medical data must adhere to stringent privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Federated learning (FL) enables the sharing of only model update parameters during collaborative training on locally stored data. However, these parameters may inadvertently enable reconstruction of the original data. Furthermore, medical data often exhibit nonindependent and nonidentically distributed (non‐IID) characteristics, impeding model training performance. To address these challenges, this paper proposes a scheme that partitions confidential data into multiple segments during FL training, ensuring that only a subset exceeding a predefined threshold can reconstruct the data. The proposed scheme guarantees enhanced security, distributed control, and fault tolerance. In addition, this paper introduces a Conditional Mutual Information (CMI) regularizer to mitigate variability in model predictions. By minimizing the Kullback–Leibler (KL) divergence between local and global feature distributions, the CMI regularizer substantially enhances performance and convergence stability. Extensive experiments conducted on the Figshare dataset with varying α‐values for data distributions validate the efficacy of the proposed model. Compared to FedAvg, FedProx, and FedDyn at α = 0.3, as well as the central model, the proposed model achieves a top‐1 accuracy of 92.94% on the Figshare dataset, surpassing FedProx, FedAvg, and FedDyn by 2.42%, 2.82%, and 3.53%, respectively. Federated IID achieves performance comparable to that of the central model, further demonstrating its viability for practical applications.

Federated learning (FL) is a novel approach to privacy‐preserving machine learning, enabling remote devices to collaborate on model training without exchanging data among clients. However, it faces several challenges, including limited client‐side processing capabilities and non‐IID data distributions. To address these challenges, we propose a partitioned FL architecture that a large CNN is divided into smaller networks, which train concurrently with other clients. Within a cluster, multiple clients concurrently train the ensemble model. The Jensen–Shannon divergence quantifies the similarity of predictions across submodels. To address discrepancies in model parameters between local and global models caused by data distribution, we propose an ensemble learning method that integrates a penalty term into the local model’s loss calculation, thereby ensuring synchronization. This method amalgamates predictions and losses across multiple submodels, effectively mitigating accuracy loss during the integration process. Extensive experiments with various Dirichlet parameters demonstrate that our system achieves accelerated convergence and enhanced performance on the CIFAR‐10 and CIFAR‐100 image classification tasks while remaining robust to partial participation, diverse datasets, and numerous clients. On the CIFAR‐10 dataset, our method outperforms FedAvg, FedProx, and SplitFed by 6%–8%; in contrast, it outperforms them by 12%–18% on CIFAR‐100.