Distributed training can facilitate the processing of large medical image datasets, and improve the accuracy and efficiency of disease diagnosis while protecting patient privacy, which is crucial for achieving efficient medical image analysis and accelerating medical research progress. This paper presents an innovative approach to medical image classification, leveraging Federated Learning (FL) to address the dual challenges of data privacy and efficient disease diagnosis. Traditional Centralized Machine Learning models, despite their widespread use in medical imaging for tasks such as disease diagnosis, raise significant privacy concerns due to the sensitive nature of patient data. As an alternative, FL emerges as a promising solution by allowing the training of a collective global model across local clients without centralizing the data, thus preserving privacy. Focusing on the application of FL in Magnetic Resonance Imaging (MRI) brain tumor detection, this study demonstrates the effectiveness of the Federated Learning framework coupled with EfficientNet-B0 and the FedAvg algorithm in enhancing both privacy and diagnostic accuracy. Through a meticulous selection of preprocessing methods, algorithms, and hyperparameters, and a comparative analysis of various Convolutional Neural Network (CNN) architectures, the research uncovers optimal strategies for image classification. The experimental results reveal that EfficientNet-B0 outperforms other models like ResNet in handling data heterogeneity and achieving higher accuracy and lower loss, highlighting the potential of FL in overcoming the limitations of traditional models. The study underscores the significance of addressing data heterogeneity and proposes further research directions for broadening the applicability of FL in medical image analysis.

翻译：分布式训练能够促进大规模医学影像数据集的处理，在保护患者隐私的同时提升疾病诊断的准确性与效率，这对于实现高效医学影像分析、加速医学研究进程至关重要。本文提出了一种创新的医学影像分类方法，利用联邦学习（Federated Learning, FL）解决数据隐私保护与高效疾病诊断的双重挑战。传统集中式机器学习模型虽广泛用于疾病诊断等医学影像任务，但因患者数据的敏感性而引发严重的隐私顾虑。作为替代方案，FL通过允许在本地客户端间训练全局联合模型而无需集中存储数据，从而保护隐私。本研究聚焦于FL在磁共振成像（MRI）脑肿瘤检测中的应用，展示了联邦学习框架结合EfficientNet-B0与FedAvg算法在增强隐私保护和诊断准确性两方面的有效性。通过精心选择预处理方法、算法及超参数，并对多种卷积神经网络（CNN）架构进行对比分析，研究揭示了图像分类的最优策略。实验结果表明，EfficientNet-B0在处理数据异质性、实现更高准确率和更低损失率方面优于ResNet等模型，凸显了FL在突破传统模型局限性方面的潜力。该研究强调了解决数据异质性的重要性，并提出了拓展FL在医学影像分析中应用范围的未来研究方向。