3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.CL2022★ 1 cited
Training Mixed-Domain Translation Models via Federated Learning
Peyman Passban, Tanya Roosta, Rahul Gupta +2
Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques. In this work, we leverage federated learning…
cs.LG2022★ 1 cited
Learnings from Federated Learning in the Real world
Christophe Dupuy, Tanya G. Roosta, Leo Long +3
Federated Learning (FL) applied to real world data may suffer from several idiosyncrasies. One such idiosyncrasy is the data distribution across devices. Data across devices could…
cs.CL2021★ 3 cited
Communication-Efficient Federated Learning for Neural Machine Translation
Tanya Roosta, Peyman Passban, Ankit Chadha
Training neural machine translation (NMT) models in federated learning (FL) settings could be inefficient both computationally and communication-wise, due to the large size of tran…