Hybrid Centralized and Distributed Learning for MEC-Equipped Satellite 6G Networks

For future networks in the 6G, it will be important to maintain a ubiquitous connection, bring processing heavy applications to remote areas, and analyze big amounts of data to efficiently provide services. To achieve such goals, the literature has utilized satellite networks to reach areas far away from the network core, and there has even been research into equipping such satellites with edge cloud servers to provide computation offloading to remote devices. However, analyzing the big data created by these devices is still a problem. One could transfer the data to a central server, but this has a high transmission cost. One could process the data through distributed machine learning, but such a technique is not as efficient as centralized learning. Thus, in this paper, we analyze the learning costs behind centralized and distributed learning and propose a hybrid solution that adaptively uses the advantages of both in a cloud server-equipped satellite network. Our proposal can identify the best learning strategy for each device based on the current scenario. Results show that the proposal is not only efficient in solving machine learning tasks, but it is also dynamic to react to different configurations while maintaining top performance.