Federated-Learning-in-Gaze-Recognition. The efficiency and generalizability of a deep learning model is based on the amount and diversity of training data. Although huge amounts of data are being collected, these data are not stored in centralized servers for further data processing. It is often infeasible to collect and share data in centralized servers due to various medical data regulations. This need for diversely distributed data and infeasible storage solutions calls for Federated Learning (FL). FL is a clever way of utilizing privately stored data in model building without the need for data sharing. The idea is to train several different models locally with the same architecture, share the model weights between the collaborators, aggregate the model weights and use the resulting global weights in furthering model building. FL is an iterative algorithm that repeats the above steps over a defined number of rounds. By doing so, we negate the need for centralized data sharing and avoid several regulations tied to it. In this work, federated learning is applied to gaze recognition, a task to identify where the doctor’s gaze at. A global model is built by repeatedly aggregating local models built from 8 local institutional data using the FL algorithm for 4 federated rounds. The results show an increase in the performance of the global model over federated rounds. The study also shows that the global model can be trained one more time locally at the end of FL on each institutional level to fine-tune the model to local data.