  Personalized FL has been widely used to cater to heterogeneity challenges
with non-IID data. A primary obstacle is considering the personalization
process from the client's perspective to preserve their autonomy. Allowing the
clients to participate in personalized FL decisions becomes significant due to
privacy and security concerns, where the clients may not be at liberty to share
private information necessary for producing good quality personalized models.
Moreover, clients with high-quality data and resources are reluctant to
participate in the FL process without reasonable incentive. In this paper, we
propose PI-FL, a one-shot personalization solution complemented by a
token-based incentive mechanism that rewards personalized training. PI-FL
outperforms other state-of-the-art approaches and can generate good-quality
personalized models while respecting clients' privacy.
