The paper explores training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and its impact on minimizing the performance gap with centralized models. It examines adaptive optimizers, loss characteristics, model initialization, and carrying over modeling setup from centralized training to FL.
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Smoothness Induced by Optimizers in Federated Learning for End-to-End ASR
In this paper, we explore the practical considerations for minimizing the performance gap in Automatic Speech Recognition (ASR) models trained using Federated Learning (FL) compared to centralized training. We focus on adaptive optimizers, loss characteristics, model initialization, and carrying over modeling setup from centralized training to FL.
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