Ditto: Fair and Robust Federated Learning Through Personalization

Understand

Fairness and robustness are two important concerns for federated learning systems.

• In this work, we identify that robustness to data and model poisoning 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 framework for personalized federated learning, Ditto, that can inherently provide fairness and robustness benefits, and develop a scalable solver for it.
• Theoretically, we analyze the ability of Ditto to achieve fairness and robustness simultaneously on a class of linear problems.