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

PI-FL: Personalized and Incentivized Federated Learning