Organizations around the world are increasingly using multiple clouds for critical workloads, which makes the accurate prediction of cloud workload a collaborative task across multiple cloud providers. Our experimental studies on four large-scale cloud workload datasets demonstrate that the workloads are highly heterogeneous, and different cloud providers have highly heterogeneous model preferences, which makes the above task challenging. Accordingly, we propose a joint privacy-preserving cloud workload prediction framework based on Federated Learning (FL), which enables the collaborative training of a workload prediction model across cloud providers with heterogeneous workloads and model preferences without exposing private workload data. To handle workload heterogeneity without privacy leakage, we first design a Generative Adversarial Network (GAN) based virtual workload dataset generation method that incorporates workloads’ temporal and resource-usage-plan-related features via temporal-aware feature calibration. Then, to handle model heterogeneity, we design a FL approach based on a hybrid local model composed of a homogeneous public model and a heterogeneous personal model. Finally, we design a Knowledge Distillation (KD) based approach to post-train the personal model with both private workload knowledge and shared workload knowledge learned from the trained global public model. Extensive experiments on various real-world workloads and actual implementations demonstrate that compared with the state-of-the-art, our framework improves workload prediction accuracy by 57.7% in average over all cloud providers.