CORE MACHINE LEARNING

Federated Multi-Task Learning for Competing Constraints

December 11, 2020

Abstract

In addition to accuracy, fairness and robustness are two critical concerns for federated learning systems. In this work, we first identify that robustness to adversarial training-time attacks and fairness, measured as the uniformity of performance across devices, are competing constraints in statistically heterogeneous networks.To address these constraints, we propose employing a simple, general multi-task learning objective, and analyze the ability of the objective to achieve a favorable trade-off between fairness and robustness. We develop a scalable solver for the objective and show that multi-task learning can enable more accurate, robust, and fair models relative to state-of-the-art baselines across a suite of federated datasets.

Download the Paper

AUTHORS

Written by

Tian Li

Shengyuan Hu

Ahmad Beirami

Virginia Smith

Publisher

Neural Information Processing Systems (NeurIPS 2020)

Research Topics

Machine Learning

Related Publications

June 14, 2020

Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA | Facebook AI Research

Many visual scenes contain text that carries crucial information, and it is thus essential to understand text in images for downstream reasoning tasks. For example, a deep water label on a warning sign warns people about the danger in the…

Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach

June 14, 2020

June 17, 2019

COMPUTER VISION

DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition | Facebook AI Research

Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming. Recent works directly leverage the motion vectors and residuals…

Zheng Shou, Xudong Lin, Yannis Kalantidis, Laura Sevilla-Lara, Marcus Rohrbach, Shih-Fu Chang, Zhicheng Yan

June 17, 2019

June 18, 2019

COMPUTER VISION

Embodied Question Answering in Photorealistic Environments with Point Cloud Perception | Facebook AI Research

To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task – Embodied Question Answering [1] in photo-realistic environments (Matterport 3D). We…

Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, Dhruv Batra

June 18, 2019

August 01, 2019

NLP

Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives | Facebook AI Research

This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large…

Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang

August 01, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.