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Sharut Gupta

Alumni

Publications

FL Games: A federated learning framework for distribution shifts
Sharut Gupta
Kartik Ahuja
Mohammad Havaei
Niladri Chatterjee
Yoshua Bengio
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However… (see more), participating clients typically each hold data from a different distribution, whereby predictive models with strong in-distribution generalization can fail catastrophically on unseen domains. In this work, we argue that in order to generalize better across non-i.i.d. clients, it is imperative to only learn correlations that are stable and invariant across domains. We propose FL Games, a game-theoretic framework for federated learning for learning causal features that are invariant across clients. While training to achieve the Nash equilibrium, the traditional best response strategy suffers from high-frequency oscillations. We demonstrate that FL Games effectively resolves this challenge and exhibits smooth performance curves. Further, FL Games scales well in the number of clients, requires significantly fewer communication rounds, and is agnostic to device heterogeneity. Through empirical evaluation, we demonstrate that FL Games achieves high out-of-distribution performance on various benchmarks.
2022-10-20
NeurIPS.cc/2022/Workshop/Federated_Learning (oral)
doi.org
openreview.net
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results
Raghav Mehta
Angelos Filos
Ujjwal Baid
Chiharu Sako
Richard McKinley
Michael Rebsamen
Katrin Dätwyler
Raphael Meier
Piotr Radojewski
Gowtham Krishnan Murugesan
Sahil Nalawade
Chandan Ganesh
Benjamin C. Wagner
Fang Frank Yu
Baowei Fei
Ananth J. Madhuranthakam
Joseph A. Maldjian
Laura Daza
Catalina Gómez
Pablo Arbeláez … (see 72 more)
Chengliang Dai
Shuo Wang
Hadrien Reynaud
Yuanhan Mo
Elsa Angelini
Yike Guo
Wenjia Bai
Subhashis Banerjee
Linmin Pei
Murat AK
Sarahi Rosas-González
Ilyess Zemmoura
Clovis Tauber
Minh H. Vu
Tufve Nyholm
Tommy Löfstedt
Laura Mora Ballestar
Veronica Vilaplana
Hugh McHugh
Gonzalo Maso Talou
Alan Wang
Jay Patel
Ken Chang
Katharina Hoebel
Mishka Gidwani
Nishanth Arun
Sharut Gupta
Mehak Aggarwal
Praveer Singh
Elizabeth R. Gerstner
Jayashree Kalpathy-Cramer
Nicolas Boutry
Alexis Huard
Lasitha Vidyaratne
Md Monibor Rahman
Khan M. Iftekharuddin
Joseph Chazalon
Elodie Puybareau
Guillaume Tochon
Jun Ma
Mariano Cabezas
Xavier Llado
Arnau Oliver
Liliana Valencia
Sergi Valverde
Mehdi Amian
Mohammadreza Soltaninejad
Andriy Myronenko
Ali Hatamizadeh
Xue Feng
Quan Dou
Nicholas Tustison
Craig Meyer
Nisarg A. Shah
Sanjay Talbar
Marc-André Weber
Abhishek Mahajan
Andras Jakab
Roland Wiest
Hassan M. Fathallah-Shaykh
Arash Nazeri
Mikhail Milchenko
Daniel Marcus
Aikaterini Kotrotsou
Rivka R. Colen
John Freymann
Justin Kirby
Christos Davatzikos
Bjoern Menze
Spyridon Bakas
Yarin Gal
Tal Arbel
2022-08-26
Machine Learning for Biomedical Imaging (published)
doi.org
arxiv.org