While hospitals routinely gather patient data, such as X-ray images, the challenge of sharing this data across multiple institutions to create a comprehensive and large dataset is hampered by privacy concerns. Consequently, this limitation affects the effectiveness of state-of-the-art deep neural networks for tasks like identifying lung diseases in medical images, as they require substantial annotated data. Federated Learning offers a solution by enabling collaborative training across multiple edge devices or sites, where updates (e.g., neural network weights) are aggregated without sharing patient data, thus maintaining privacy. This work introduces a federated-learning-based approach for automatically detecting lung diseases in chest X-ray images, focusing on preserving data privacy and enhancing robustness. Our approach follows the federated learning protocol: decentralized training of neural networks on data from multiple sites (hospitals) and centralized aggregation of knowledge in the server. The solution presents promising results in identifying fourteen lung diseases compared to three baselines within a simulated environment comprising chest X-ray images from five distinct sites.