  Recent studies have used speech signals to assess depression. However, speech
features can lead to serious privacy concerns. To address these concerns, prior
work has used privacy-preserving speech features. However, using a subset of
features can lead to information loss and, consequently, non-optimal model
performance. Furthermore, prior work relies on a centralized approach to
support continuous model updates, posing privacy risks. This paper proposes to
use Federated Learning (FL) to enable decentralized, privacy-preserving speech
analysis to assess depression. Using an existing dataset (DAIC-WOZ), we show
that FL models enable a robust assessment of depression with only 4--6%
accuracy loss compared to a centralized approach. These models also outperform
prior work using the same dataset. Furthermore, the FL models have short
inference latency and small memory footprints while being energy-efficient.
These models, thus, can be deployed on mobile devices for real-time,
continuous, and privacy-preserving depression assessment at scale.
