  Federated learning (FL) is an effective mechanism for data privacy in
recommender systems by running machine learning model training on-device. While
prior FL optimizations tackled the data and system heterogeneity challenges
faced by FL, they assume the two are independent of each other. This
fundamental assumption is not reflective of real-world, large-scale recommender
systems -- data and system heterogeneity are tightly intertwined. This paper
takes a data-driven approach to show the inter-dependence of data and system
heterogeneity in real-world data and quantifies its impact on the overall model
quality and fairness. We design a framework, RF^2, to model the
inter-dependence and evaluate its impact on state-of-the-art model optimization
techniques for federated recommendation tasks. We demonstrate that the impact
on fairness can be severe under realistic heterogeneity scenarios, by up to
15.8--41x compared to a simple setup assumed in most (if not all) prior work.
It means when realistic system-induced data heterogeneity is not properly
modeled, the fairness impact of an optimization can be downplayed by up to 41x.
The result shows that modeling realistic system-induced data heterogeneity is
essential to achieving fair federated recommendation learning. We plan to
open-source RF^2 to enable future design and evaluation of FL innovations.
