Mixture-of-experts (MoE) models provide a flexible statistical framework for modeling heterogeneity and nonlinear relationships. In many modern applications, however, datasets are naturally distributed across multiple machines due to storage, computational, or governance constraints. We consider a distributed model aggregation setting in which local MoE models are trained independently on decentralized datasets and subsequently combined into a global estimator. Aggregating MoE models is challenging because standard averaging produces models that do not preserve the MoE structure, and therefore do not yield estimates of the global model parameters. To address this issue, we propose a principled aggregation framework based on optimal transport that constructs a reduced global MoE estimator by minimizing a transportation divergence between the collection of local estimators and the aggregated model. An efficient majorization--minimization (MM) algorithm is derived to solve the resulting optimization problem. The method requires only a single communication step from local machines to a central server, making it a frugal distributed learning approach particularly attractive for large-scale settings where communication costs are a major bottleneck. We further establish statistical guarantees for the aggregated estimator, including consistency under standard assumptions on the local estimators. Experiments on synthetic and real datasets demonstrate that the approach achieves performance comparable to centralized training while significantly reducing computation time. The source codes are publicly available on Github.