  In Federated Learning (FL), the clients learn a single global model (FedAvg)
through a central aggregator. In this setting, the non-IID distribution of the
data across clients restricts the global FL model from delivering good
performance on the local data of each client. Personalized FL aims to address
this problem by finding a personalized model for each client. Recent works
widely report the average personalized model accuracy on a particular data
split of a dataset to evaluate the effectiveness of their methods. However,
considering the multitude of personalization approaches proposed, it is
critical to study the per-user personalized accuracy and the accuracy
improvements among users with an equitable notion of fairness. To address these
issues, we present a set of performance and fairness metrics intending to
assess the quality of personalized FL methods. We apply these metrics to four
recently proposed personalized FL methods, PersFL, FedPer, pFedMe, and
Per-FedAvg, on three different data splits of the CIFAR-10 dataset. Our
evaluations show that the personalized model with the highest average accuracy
across users may not necessarily be the fairest. Our code is available at
https://tinyurl.com/1hp9ywfa for public use.
