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Federated Learning for Keyword Spotting

arXiv 1810.05512 ↗First posted 9 Oct 2018eess.AS cs.CL cs.LGcited by 95

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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.

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

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Citing papers per year: 2021: 29; 2022: 26; 2023: 18; 2024: 11; 2025: 9; 2026: 2.

95 papers.

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  1. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 85
  2. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  3. 2021The VoicePrivacy 2020 Challenge: Results and findingscited by 46
  4. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 43
  5. 2021Deep Spoken Keyword Spotting: An Overviewcited by 43
  6. 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
  7. 2021Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learningcited by 31
  8. 2021End-to-End Speech Recognition from Federated Acoustic Modelscited by 30
  9. 2021FEDZIP: A Compression Framework for Communication-Efficient Federated Learningcited by 27
  10. 2022FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learningcited by 27
  11. 2023On the Vulnerability of Backdoor Defenses for Federated Learningcited by 27
  12. 2022Accelerated Federated Learning with Decoupled Adaptive Optimizationcited by 23
38 more
  1. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 21
  2. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 21
  3. 2021EasyFL: A Low-code Federated Learning Platform For Dummiescited by 20
  4. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  5. 2021FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robotscited by 18
  6. 2024FedMef: Towards Memory-efficient Federated Dynamic Pruningcited by 14
  7. 2022Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labelscited by 13
  8. 2022A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairnesscited by 13
  9. 2023MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulationcited by 13
  10. 2022Training Mixed-Domain Translation Models via Federated Learningcited by 12
  11. 2021Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applicationscited by 11
  12. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  13. 2021PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classificationcited by 10
  14. 2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
  15. 2021FedMe: Federated Learning via Model Exchangecited by 10
  16. 2021Privacy attacks for automatic speech recognition acoustic models in a federated learning frameworkcited by 10
  17. 2022Federated Self-supervised Speech Representations: Are We There Yet?cited by 10
  18. 2023FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategycited by 10
  19. 2022Production federated keyword spotting via distillation, filtering, and joint federated-centralized trainingcited by 9
  20. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  21. 2021A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter Itcited by 8
  22. 2021Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Datacited by 8
  23. 2021A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Datacited by 8
  24. 2022Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learningcited by 7
  25. 2023Federated Learning for ASR based on Wav2vec 2.0cited by 7
  26. 2024Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensorscited by 7
  27. 2022Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learningcited by 6
  28. 2022Federated Domain Adaptation for ASR with Full Self-Supervisioncited by 6
  29. 2022A Benchmark for Federated Hetero-Task Learningcited by 6
  30. 2023A Privacy Preserving System for Movie Recommendations Using Federated Learningcited by 6
  31. 2025FedSV: Byzantine-Robust Federated Learning via Shapley Valuecited by 6
  32. 2021Federated Self-Training for Semi-Supervised Audio Recognitioncited by 5
  33. 2021OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Frameworkcited by 5
  34. 2024Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devicescited by 5
  35. 2025A Novel Hat-Shaped Device-Cloud Collaborative Inference Framework for Large Language Modelscited by 5
  36. 2021Dynamic Gradient Aggregation for Federated Domain Adaptationcited by 4
  37. 2021Zero-Shot Federated Learning with New Classes for Audio Classificationcited by 4
  38. 2021Accelerating Federated Learning with a Global Biased Optimisercited by 4

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