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.

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AUTHORS

Written by

Tian Li

Shengyuan Hu

Ahmad Beirami

Virginia Smith

Publisher

Neural Information Processing Systems (NeurIPS 2020)

Research Topics

Machine Learning

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