  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. Empirically, across a suite of
federated datasets, we show that Ditto not only achieves competitive
performance relative to recent personalization methods, but also enables more
accurate, robust, and fair models relative to state-of-the-art fair or robust
baselines.
