5 citations · 5 across the 2 of their papers we have counts for
3 papers
eess.AS2022
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training
Andrew Hard, Kurt Partridge, Neng Chen +9
We trained a keyword spotting model using federated learning on real user devices and observed significant improvements when the model was deployed for inference on phones. To comp…
cs.LG2021★ 5 cited
Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift
Sean Augenstein, Andrew Hard, Kurt Partridge +1
With privacy as a motivation, Federated Learning (FL) is an increasingly used paradigm where learning takes place collectively on edge devices, each with a cache of user-generated…
eess.AS2020
Training Keyword Spotting Models on Non-IID Data with Federated Learning
Andrew Hard, Kurt Partridge, Cameron Nguyen +5
We demonstrate that a production-quality keyword-spotting model can be trained on-device using federated learning and achieve comparable false accept and false reject rates to a ce…