# Federated Learning for Keyword Spotting

arXiv: https://arxiv.org/abs/1810.05512 · HTML: https://mlsanity.com/paper/1810.05512/

## Key facts

- Cited by 95 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2018.

## Built on by (most cited first, 30 of 95)

- [FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://mlsanity.com/paper/2104.08815/) (2021)
- [FederatedScope: A Flexible Federated Learning Platform for Heterogeneity](https://mlsanity.com/paper/2204.05011/) (2022)
- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [Federated Learning Meets Natural Language Processing: A Survey](https://mlsanity.com/paper/2107.12603/) (2021)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing](https://mlsanity.com/paper/2203.03550/) (2022)
- [Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning](https://mlsanity.com/paper/2102.03198/) (2021)
- [End-to-End Speech Recognition from Federated Acoustic Models](https://mlsanity.com/paper/2104.14297/) (2021)
- [FEDZIP: A Compression Framework for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.01593/) (2021)
- [FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2210.07615/) (2022)
- [On the Vulnerability of Backdoor Defenses for Federated Learning](https://mlsanity.com/paper/2301.08170/) (2023)
- [Accelerated Federated Learning with Decoupled Adaptive Optimization](https://mlsanity.com/paper/2207.07223/) (2022)
- [Privacy and Efficiency of Communications in Federated Split Learning](https://mlsanity.com/paper/2301.01824/) (2023)
- [FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs](https://mlsanity.com/paper/2306.04959/) (2023)
- [EasyFL: A Low-code Federated Learning Platform For Dummies](https://mlsanity.com/paper/2105.07603/) (2021)
- [Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework](https://mlsanity.com/paper/2409.11585/) (2024)
- [FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots](https://mlsanity.com/paper/2101.03705/) (2021)
- [FedMef: Towards Memory-efficient Federated Dynamic Pruning](https://mlsanity.com/paper/2403.14737/) (2024)
- [Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels](https://mlsanity.com/paper/2208.09378/) (2022)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation](https://mlsanity.com/paper/2311.13348/) (2023)
- [Training Mixed-Domain Translation Models via Federated Learning](https://mlsanity.com/paper/2205.01557/) (2022)
- [Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications](https://mlsanity.com/paper/2103.13266/) (2021)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification](https://mlsanity.com/paper/2104.01271/) (2021)
- [Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis](https://mlsanity.com/paper/2110.07567/) (2021)
- [FedMe: Federated Learning via Model Exchange](https://mlsanity.com/paper/2110.07868/) (2021)
- [Privacy attacks for automatic speech recognition acoustic models in a federated learning framework](https://mlsanity.com/paper/2111.03777/) (2021)
- [Federated Self-supervised Speech Representations: Are We There Yet?](https://mlsanity.com/paper/2204.02804/) (2022)
- [FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy](https://mlsanity.com/paper/2310.09789/) (2023)

## Abstract (arXiv)

We propose a practical approach based on federated learning to solve out-of-domain issues with continuously running embedded speech-based models such as wake word detectors. We conduct an extensive empirical study of the federated averaging algorithm for the "Hey Snips" wake word based on a crowdsourced dataset that mimics a federation of wake word users. We empirically demonstrate that using an adaptive averaging strategy inspired from Adam in place of standard weighted model averaging highly reduces the number of communication rounds required to reach our target performance. The associated upstream communication costs per user are estimated at 8 MB, which is a reasonable in the context of smart home voice assistants. Additionally, the dataset used for these experiments is being open sourced with the aim of fostering further transparent research in the application of federated learning to speech data.