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Enabling On-Device Training of Speech Recognition ...
arXiv
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由 D Guliani 著作2021被引用 19 次 — We propose using federated dropout to reduce the size of client models while training a full-size model server-side. We provide empirical ...
Enabling On-Device Training of Speech Recognition ...
IEEE SigPort
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We argued that. Federated Dropout is a promising technique to reduce this cost and explored various configurations to improve its effectiveness. We illustrated ...
enabling on-device training of speech recognition models ...
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由 D Guliani 著作2022被引用 19 次 — Federated Dropout [12] (FD) is a technique developed to enable training larger models under FL by reducing the size of models trained on clients. It works ...
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Enabling On-Device Training of Speech Recognition ...
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2024年9月8日 — We propose using federated dropout to reduce the size of client models while training a full-size model server-side. We provide empirical ...
[PDF] Enabling On-Device Training of Speech Recognition ...
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This work proposes using federated dropout to reduce the size of client models while training a full-size model server-side, and provides empirical evidence ...
Enabling On-Device Training of Speech Recognition ...
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Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default. This presents ...
[PDF] Training Speech Recognition Models with Federated ...
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Enabling On-Device Training of Speech Recognition Models With Federated Dropout · Separate But Together: Unsupervised Federated Learning for Speech Enhancement ...
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Giovanni Motta
Papers With Code
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We propose using federated learning, a decentralized on-device learning paradigm, to train speech recognition models. Federated Learning · speech-recognition +1.
On-Device Federated Learning of ASR Models - Yonghui Xiao
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2024年4月24日 — Xiao, F. Beaufays, and G. Motta, “Enabling on-device training of speech recognition models with federated dropout,” arXiv:2110.03634, (2021).
Does Federated Dropout Actually Work?
CVF Open Access
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由 G Cheng 著作2022被引用 23 次 — Federated Learning is the distributed learning setting where a central server coordinates with client devices to train machine learning models in a ...
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