  Traditional Federated Learning (FL) methods typically train a single global
model collaboratively without exchanging raw data. In contrast, Personalized
Federated Learning (PFL) techniques aim to create multiple models that are
better tailored to individual clients' data. We present a novel personalization
approach based on stacked generalization where clients directly send each other
privacy-preserving models to be used as base models to train a meta-model on
private data. Our approach is flexible, accommodating various
privacy-preserving techniques and model types, and can be applied in
horizontal, hybrid, and vertically partitioned federations. Additionally, it
offers a natural mechanism for assessing each client's contribution to the
federation. Through comprehensive evaluations across diverse simulated data
heterogeneity scenarios, we showcase the effectiveness of our method.
