  Data heterogeneity across participating devices poses one of the main
challenges in federated learning as it has been shown to greatly hamper its
convergence time and generalization capabilities. In this work, we address this
limitation by enabling personalization using multiple user-centric aggregation
rules at the parameter server. Our approach potentially produces a personalized
model for each user at the cost of some extra downlink communication overhead.
To strike a trade-off between personalization and communication efficiency, we
propose a broadcast protocol that limits the number of personalized streams
while retaining the essential advantages of our learning scheme. Through
simulation results, our approach is shown to enjoy higher personalization
capabilities, faster convergence, and better communication efficiency compared
to other competing baseline solutions.
