Abstract: To register an object for subsequent identification by an artificial reality headset, the headset receives an input from a user to enter a registration mode. The headset includes a plurality of imaging devices capturing images of a local area surrounding the headset. An eye tracking unit of the headset determines a gaze direction of the user, and the headset identifies an object in the local area where the user's gaze is directed and visually distinguishes the identified object to the user. In response to receiving a confirmation from the user to register the identified object, one or more images of the identified object are captured by the imaging devices and used to train an instance classifier to identify the identified object. The headset notifies the user when the identified object has been registered.

Abstract: A federated machine-learning system includes a global server and client devices. The server receives updates of weight factor dictionaries and factor strengths vectors from the clients, and generates a globally updated weight factor dictionary and a globally updated factor strengths vector. A client device selects a group of parameters from a global group of parameters, and trains a model using a dataset of the client device and the group of selected parameters. The client device sends to the server a client-updated weight factor dictionary and a client-updated factor strengths vector. The client device receives the globally updated weight factor dictionary and the globally updated factor strengths vector, and retrains the model using the dataset of the client device, the group of parameters selected by the client device, and the globally updated weight factor dictionary and the globally updated factor strengths vector.