Federated Learning for Keyword Spotting

Luque, David; Coucke, Alice; Lavril, Thibaut; Gisselbrecht, Thibault; Dureau, Joseph

doi:10.1109/icassp.2019.8683546

Cited by 229 publications

(126 citation statements)

References 7 publications

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“…Further, we show that applying smoothing before max pooling is highly important for achieving accuracy better than the baseline. The proposed approach provides further benefits of reducing dependence on LVCSR to provide phoneme level alignments, which is desirable for embedded learning scenarios, like on-device learning [20] [21].…”

Section: Resultsmentioning

confidence: 99%

Learning to Detect Keyword Parts and Whole by Smoothed Max Pooling

Park

Violette

Subrahmanya

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose smoothed max pooling loss and its application to keyword spotting systems. The proposed approach jointly trains an encoder (to detect keyword parts) and a decoder (to detect whole keyword) in a semi-supervised manner. The proposed new loss function allows training a model to detect parts and whole of a keyword, without strictly depending on frame-level labeling from LVCSR (Large vocabulary continuous speech recognition), making further optimization possible. The proposed system outperforms the baseline keyword spotting model in [1] due to increased optimizability. Further, it can be more easily adapted for on-device learning applications due to reduced dependency on LVCSR.

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Section: Resultsmentioning

confidence: 99%

Learning to Detect Keyword Parts and Whole by Smoothed Max Pooling

Park

Violette

Subrahmanya

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Like the previous application, FL was also applied for keyboard prediction and keyword spotting [155]. However, the approaches are different.…”

Section: ) Mobile Keyboard Predictionmentioning

confidence: 99%

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

et al. 2020

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This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

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“…Even Apple is using Federated Learning in iOS 13 [13], for various applications like the vocal classifier for "Hey Siri" [14] and QuickType keyboard. Other applications include Federated Learning for medical research [15] and the detection of hot words [16].…”

Section: Related Literaturementioning

confidence: 99%

Survey on Federated Learning Towards Privacy Preserving AI

Kurupathi¹,

Maaß²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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One of the significant challenges of Artificial Intelligence (AI) and Machine learning models is to preserve data privacy and to ensure data security. Addressing this problem lead to the application of Federated Learning (FL) mechanism towards preserving data privacy. Preserving user privacy in the European Union (EU) has to abide by the General Data Protection Regulation (GDPR). Therefore, exploring the machine learning models for preserving data privacy has to take into consideration of GDPR. In this paper, we present in detail understanding of Federated Machine Learning, various federated architectures along with different privacy-preserving mechanisms. The main goal of this survey work is to highlight the existing privacy techniques and also propose applications of Federated Learning in Industries. Finally, we also depict how Federated Learning is an emerging area of future research that would bring a new era in AI and Machine learning.

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Federated Learning for Keyword Spotting

Cited by 229 publications

References 7 publications

Learning to Detect Keyword Parts and Whole by Smoothed Max Pooling

Learning to Detect Keyword Parts and Whole by Smoothed Max Pooling

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

Survey on Federated Learning Towards Privacy Preserving AI

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