Federated Multi-Task Learning for Competing Constraints

12/08/2020
by   Tian Li, et al.
0

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 tradeoff 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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset