FlyHash is a locality-sensitive hashing algorithm inspired by the nervous system of the Drosophila fly. It has demonstrated to be particularly effective for similarity search, especially in the federated context where multiple players collaborate to solve a statistical learning task. FlyHash mainly relies on a process called winner-take-all, which is used to binarize information. However, the implementation of this process is a major challenge and limits the algorithm's usage in processing large data streams. In this paper, we propose a simple algorithm to make the winner-take-all operation efficient on GPUs. We create a FlyHash adaptation suitable for the CUDA architecture. We assess the speed of this version experimentally and present a comparison with the CPU version of FlyHash.