Privacy Sensitive Speech Analysis Using Federated Learning to Assess Depression

TL;DR

This paper introduces federated learning for privacy-preserving depression assessment via speech analysis, achieving comparable accuracy to centralized methods while enhancing privacy, efficiency, and suitability for mobile deployment.

Contribution

It demonstrates that federated learning enables effective, privacy-preserving depression detection from speech with minimal accuracy loss and improved deployment feasibility.

Findings

FL models have only 4-6% accuracy loss compared to centralized models.

FL models outperform prior work on the same dataset.

Models are energy-efficient with low latency and small memory footprint.

Abstract

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.