no code implementations • 18 Jul 2022 • MingBin Xu, Congzheng Song, Ye Tian, Neha Agrawal, Filip Granqvist, Rogier Van Dalen, Xiao Zhang, Arturo Argueta, Shiyi Han, Yaqiao Deng, Leo Liu, Anmol Walia, Alex Jin
Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP.
During private federated learning of the language model, we sample from the model, train a new tokenizer on the sampled sequences, and update the model embeddings.
This paper introduces an algorithm to enforce group fairness in private federated learning, where users' data does not leave their devices.
no code implementations • 16 Feb 2021 • Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier Van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, Sudeep Agarwal, Julien Freudiger, Andrew Byde, Abhishek Bhowmick, Gaurav Kapoor, Si Beaumont, Áine Cahill, Dominic Hughes, Omid Javidbakht, Fei Dong, Rehan Rishi, Stanley Hung
We describe the design of our federated task processing system.
From these features, the model predicts speaker characteristic labels considered useful as side information.