Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework

• URL: http://arxiv.org/abs/2010.15965v2
• Date: Fri, 14 May 2021 18:49:19 GMT
• Title: Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework
• Authors: Dhruv Guliani, Francoise Beaufays, Giovanni Motta
• Abstract summary: We propose using federated learning, a decentralized on-device learning paradigm, to train speech recognition models. By performing epochs of training on a per-user basis, federated learning must incur the cost of dealing with non-IID data distributions.
• Score: 4.125187280299247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose using federated learning, a decentralized on-device learning paradigm, to train speech recognition models. By performing epochs of training on a per-user basis, federated learning must incur the cost of dealing with non-IID data distributions, which are expected to negatively affect the quality of the trained model. We propose a framework by which the degree of non-IID-ness can be varied, consequently illustrating a trade-off between model quality and the computational cost of federated training, which we capture through a novel metric. Finally, we demonstrate that hyper-parameter optimization and appropriate use of variational noise are sufficient to compensate for the quality impact of non-IID distributions, while decreasing the cost.

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