This paper introduces a novel fuzzy association rule mining algorithm explicitly developed for federated environments. With exponential growth in datasets and increasing data privacy concerns, solutions such as federated learning have become at the forefront of secure and efficient data analysis. However, efficiently finding meaningful and relevant patterns in data across decentralized databases remains challenging. To address this, we propose integrating fuzzy logic with association rule mining in a federated setting. The ability of fuzzy logic to handle uncertainty and nuance in data combined with the distributed data mining process of federated systems, creates an efficient, secure, and powerful tool for pattern discovery. Our proposed algorithm respects data privacy and effectively manages communication overhead, an innate challenge in federated systems. Experimental results demonstrate the efficacy of the proposed algorithm. The system has significant implications for the healthcare sector, where data volume and privacy concerns are paramount.