# Speech Privacy Leakage from Shared Gradients in Distributed Learning

arXiv: https://arxiv.org/abs/2302.10441 · HTML: https://mlsanity.com/paper/2302.10441/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [See through Gradients: Image Batch Recovery via GradInversion](https://mlsanity.com/paper/2104.07586/) (2021)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage](https://mlsanity.com/paper/2203.15696/) (2022)
- [Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework](https://mlsanity.com/paper/2010.15965/) (2020)
- [Training Keyword Spotting Models on Non-IID Data with Federated Learning](https://mlsanity.com/paper/2005.10406/) (2020)
- [Improving on-device speaker verification using federated learning with privacy](https://mlsanity.com/paper/2008.02651/) (2020)
- [Production federated keyword spotting via distillation, filtering, and joint federated-centralized training](https://mlsanity.com/paper/2204.06322/) (2022)
- [A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It](https://mlsanity.com/paper/2104.07815/) (2021)

## Built on by (most cited first, 1 of 1)

- [FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs](https://mlsanity.com/paper/2604.06297/) (2026)

## Abstract (arXiv)

Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis. However, such frameworks are vulnerable to privacy leakage attacks from shared gradients. Despite extensive efforts in the image domain, the exploration of speech privacy leakage from gradients is quite limited. In this paper, we explore methods for recovering private speech/speaker information from the shared gradients in distributed learning settings. We conduct experiments on a keyword spotting model with two different types of speech features to quantify the amount of leaked information by measuring the similarity between the original and recovered speech signals. We further demonstrate the feasibility of inferring various levels of side-channel information, including speech content and speaker identity, under the distributed learning framework without accessing the user's data.