  Personalized federated learning (FL) facilitates collaborations between
multiple clients to learn personalized models without sharing private data. The
mechanism mitigates the statistical heterogeneity commonly encountered in the
system, i.e., non-IID data over different clients. Existing personalized
algorithms generally assume all clients volunteer for personalization. However,
potential participants might still be reluctant to personalize models since
they might not work well. In this case, clients choose to use the global model
instead. To avoid making unrealistic assumptions, we introduce the
personalization rate, measured as the fraction of clients willing to train
personalized models, into federated settings and propose DyPFL. This
dynamically personalized FL technique incentivizes clients to participate in
personalizing local models while allowing the adoption of the global model when
it performs better. We show that the algorithmic pipeline in DyPFL guarantees
good convergence performance, allowing it to outperform alternative
personalized methods in a broad range of conditions, including variation in
heterogeneity, number of clients, local epochs, and batch sizes.
