AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT

no code implementations • 23 Mar 2023 • Edward H. Lee, Brendan Kelly, Emre Altinmakas, Hakan Dogan, Maryam Mohammadzadeh, Errol Colak, Steve Fu, Olivia Choudhury, Ujjwal Ratan, Felipe Kitamura, Hernan Chaves, Jimmy Zheng, Mourad Said, Eduardo Reis, Jaekwang Lim, Patricia Yokoo, Courtney Mitchell, Golnaz Houshmand, Marzyeh Ghassemi, Ronan Killeen, Wendy Qiu, Joel Hayden, Farnaz Rafiee, Chad Klochko, Nicholas Bevins, Faeze Sazgara, S. Simon Wong, Michael Moseley, Safwan Halabi, Kristen W. Yeom

While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources.

Federated Learning

NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU

no code implementations • 24 Sep 2021 • Edward H. Lee, Mario Michael Krell, Alexander Tsyplikhin, Victoria Rege, Errol Colak, Kristen W. Yeom

We also provide two extensions: 1) DPSGD for pipelined models and 2) per-layer clipping that is 15x faster than the Opacus implementation on 8x A100s.

Convolutional Neural Networks using Logarithmic Data Representation

1 code implementation • 3 Mar 2016 • Daisuke Miyashita, Edward H. Lee, Boris Murmann

In this paper we propose a new data representation that enables state-of-the-art networks to be encoded to 3 bits with negligible loss in classification performance.

General Classification