Privacy-preserving Speech-based Depression Diagnosis via Federated Learning

Abstract

In this work, we demonstrate for the first time that speech-based depression diagnosis models can be trained in a privacy-preserving way using federated learning, which enables collaborative model training while keeping the private speech data decentralized on clients’ devices.

Publication
In IEEE Engineering in Medicine and Biology Society 2022