Federated Learning

A machine-learning approach in which a shared model is trained across many user devices without the raw training data ever leaving those devices: each device computes updates locally and sends only model parameters or gradients to a central server for aggregation. Federated learning is often combined with differential privacy and secure aggregation to limit what can be inferred about any individual user. In accessibility, federated learning is a candidate technique for personalizing speech recognition, AAC suggestion models, gesture recognizers, or image-description systems to disabled users without uploading sensitive on-device speech, video, or text - a privacy property that matters especially when models are trained on a user's own communication.