Multicenter imaging data offer a valuable opportunity to develop robust deep learning models, particularly in rare diseases such as Primary Central Nervous System Lymphoma (PCNSL), where single-center data are often insufficient. However, heterogeneity in imaging protocols, scanner types, and acquisition settings introduces domain shifts that hinder model generalization. Synthesizing a unified target modality is a promising solution to harmonize such data. While previous studies have explored one-to-one or paired translation methods, these are either non-scalable or require unavailable data pairs. Other approaches generate synthetic latent domains with limited clinical interpretability and high computational cost. To address these limitations, we propose InStyleGAN, an unpaired harmonization framework using a style-guided 3D CycleGAN tailored for PCNSL. Our key contributions include: (i) synthesizing a target modality from heterogeneous PET data using a 3D CycleGAN backbone; (ii) leveraging unpaired PET data with significant inter-site variations in distribution, structure and style; (iii) enforcing target-style distribution alignment via Adaptive Instance Normalization to relax the optimization. Furthermore, we introduce a novel Style-Consistency loss to better preserve content while learning the style of the target modality. Experiments demonstrate that InStyleGAN outperforms existing variants in harmonizing PET data across centers. To our knowledge, this is the first dedicated framework for multicenter PET harmonization in PCNSL.