  Statistical heterogeneity across clients in a Federated Learning (FL) system
increases the algorithm convergence time and reduces the generalization
performance, resulting in a large communication overhead in return for a poor
model. To tackle the above problems without violating the privacy constraints
that FL imposes, personalized FL methods have to couple statistically similar
clients without directly accessing their data in order to guarantee a
privacy-preserving transfer. In this work, we design user-centric aggregation
rules at the parameter server (PS) that are based on readily available gradient
information and are capable of producing personalized models for each FL
client. The proposed aggregation rules are inspired by an upper bound of the
weighted aggregate empirical risk minimizer. Secondly, we derive a
communication-efficient variant based on user clustering which greatly enhances
its applicability to communication-constrained systems. Our algorithm
outperforms popular personalized FL baselines in terms of average accuracy,
worst node performance, and training communication overhead.
