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Found 5 papers, 1 papers with code
pfl-research: simulation framework for accelerating research in Private Federated Learning
Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to the server and other participants.
Federated Learning with Differential Privacy for End-to-End Speech Recognition
($4. 5$, $10^{-9}$)-$\textbf{DP}$) with a 1. 3% (resp.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR
In this paper, we start by training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and examining the fundamental considerations that can be pivotal in minimizing the performance gap in terms of word error rate between models trained using FL versus their centralized counterpart.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Population Expansion for Training Language Models with Private Federated Learning
Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee.
An Application of a Multivariate Estimation of Distribution Algorithm to Cancer Chemotherapy
We hypothesise that this is caused by the more sophisticated algorithm being impeded by the large number of interactions in the problem which are unnecessary for its solution.
