# Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition

arXiv: https://arxiv.org/abs/2506.11069 · HTML: https://mlsanity.com/paper/2506.11069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 28 of 28)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [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)
- [End-to-End Speech Recognition from Federated Acoustic Models](https://mlsanity.com/paper/2104.14297/) (2021)
- [Speaker adaptation for Wav2vec2 based dysarthric ASR](https://mlsanity.com/paper/2204.00770/) (2022)
- [Federated Acoustic Modeling For Automatic Speech Recognition](https://mlsanity.com/paper/2102.04429/) (2021)
- [Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2202.10290/) (2022)
- [Recent Progress in the CUHK Dysarthric Speech Recognition System](https://mlsanity.com/paper/2201.05845/) (2022)
- [Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation](https://mlsanity.com/paper/2401.00662/) (2024)
- [Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2205.06445/) (2022)
- [Federated Learning in ASR: Not as Easy as You Think](https://mlsanity.com/paper/2109.15108/) (2021)
- [Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling](https://mlsanity.com/paper/2203.08810/) (2022)
- [Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection](https://mlsanity.com/paper/2206.13232/) (2022)
- [Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages](https://mlsanity.com/paper/2305.11284/) (2023)
- [Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2407.13782/) (2024)
- [Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR](https://mlsanity.com/paper/2309.13102/) (2023)
- [Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks](https://mlsanity.com/paper/2401.10070/) (2024)
- [Federated Learning with Intermediate Representation Regularization](https://mlsanity.com/paper/2210.15827/) (2022)
- [Sparse Training for Federated Learning with Regularized Error Correction](https://mlsanity.com/paper/2312.13795/) (2023)
- [Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2408.11873/) (2024)
- [Phone-purity Guided Discrete Tokens for Dysarthric Speech Recognition](https://mlsanity.com/paper/2501.04379/) (2025)

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

- [Towards Personalized Federated Learning for Dysarthric Speech Recognition](https://mlsanity.com/paper/2606.13253/) (2026)

## Abstract (arXiv)

Accurate recognition of dysarthric and elderly speech remains challenging to date. While privacy concerns have driven a shift from centralized approaches to federated learning (FL) to ensure data confidentiality, this further exacerbates the challenges of data scarcity, imbalanced data distribution and speaker heterogeneity. To this end, this paper conducts a systematic investigation of regularized FL techniques for privacy-preserving dysarthric and elderly speech recognition, addressing different levels of the FL process by 1) parameter-based, 2) embedding-based and 3) novel loss-based regularization. Experiments on the benchmark UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest that regularized FL systems consistently outperform the baseline FedAvg system by statistically significant WER reductions of up to 0.55% absolute (2.13% relative). Further increasing communication frequency to one exchange per batch approaches centralized training performance.