Resource allocation has been always a major concern of cloud providers. Workload prediction can effectively improve resource utility by providing information about resources available in the future. Machine learning-based workload prediction has been widely studied and deployed in large-scale clouds. However, workload prediction in Inter-Cloud environments has not been considered. Since more and more cloud services are orchestrated by containers (or virtual machines) distributed in multiple clouds, intelligent models for workload prediction should be trained based on trace data across clouds. Due to concerns raised by data privacy, the trace data of clouds may not be shared with each other, and general distributed training is not applicable. In this paper, we propose a general and flexible framework, namely EFL-WP, for training workload prediction models in the Inter-Cloud environment. EFL-WP adopts the federated learning approach and allows cloud providers to collaborate to train prediction models without sharing traces. EFL-WP considers the difference among workloads in different cloud (i.e., the Non-IID characteristic of traces) by using two novel techniques: participant selection mechanism and multi-global models aggregation. The former can prevent some local models trained on non-IID traces from participating in global aggregation, which is beneficial to the global model. The latter allows the coordinator to aggregate several global models according to the difference among local models. To further improve accuracy, EFL-WP adopts an ensemble inference strategy. Experimental results show that the proposed framework can be superior to baselines on both Alibaba dataset and Tencent Games Traces.