# Privacy Sensitive Speech Analysis Using Federated Learning to Assess Depression

arXiv: https://arxiv.org/abs/2205.00111 · HTML: https://mlsanity.com/paper/2205.00111/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2022.

## Built on by (most cited first, 8 of 8)

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- [Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements](https://mlsanity.com/paper/2506.09707/) (2025)
- [On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts](https://mlsanity.com/paper/2410.13709/) (2024)
- [The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions](https://mlsanity.com/paper/2501.11714/) (2025)
- [AI Safety Training Can be Clinically Harmful](https://mlsanity.com/paper/2604.23445/) (2026)
- [Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data](https://mlsanity.com/paper/2602.15478/) (2026)

## Abstract (arXiv)

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.