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Lung cancer remains one of the leading causes of cancer-related deaths worldwide, with Non-Small Cell Lung Cancer (NSCLC) accounting for approximately 85% of all cases. Accurate histological subtype classification of NSCLC is crucial for personalized treatment planning and improving patient outcomes. Developing robust classification models for NSCLC subtypes often requires large, diverse datasets, which can be challenging to obtain due to privacy concerns and data silos. This study proposes an approach combining federated learning with triplet loss to address these challenges. We evaluated our method’s performance in classifying NSCLC subtypes using data from multiple institutions while preserving privacy. Our experiments compared the proposed federated learning approach with triplet loss against alternative methods, including local training and softmax loss. Results demonstrated that our federated learning approach with triplet loss consistently outperformed other methods across key metrics. The combination of federated learning and triplet loss showed synergistic effects, leveraging external datasets to improve model performance while maintaining data confidentiality. The source code for the implementation described in this paper is available at https://github.com/aksufatih/federated-triplet-histology.