In distributed scenarios, the implementation of multi-view 3D object recognition algorithms through federated analytics (FA) frequently results in communication costs induced by feature aggregation that significantly contribute to the overall latency and energy consumption of the system. Furthermore, the transmission of features may give rise to concerns regarding privacy. This paper presents a federated multi-view 3D object recognition scheme based on compressed learning (called as CFMVOR). Firstly, in order to reduce the communication overhead, compressed learning is applied in order to extract features directly from compressed sensing measurements. Concurrently, the measurements appear visually similar to noise, thereby providing visual privacy. Subsequently, we present the hierarchical feature aggregation (HFA) method, which reduces the volume of communication features without compromising model accuracy. Finally, to address the observed threats to FA, we introduce local differential privacy (LDP) and enhance its perturbation mechanism to achieve feature-level adaptive LDP protection. Experiments on the ModelNet40 and ModelNet10 datasets demonstrate that our scheme, with a minimum compression ratio of 0.1, can significantly reduce communication overhead and, with a lower privacy budget \(\epsilon = 5\) , maintain accuracy above 90% on both datasets.