Hybrid Centralized and Distributed Learning for MEC-Equipped Satellite 6G Networks
Hybrid Centralized and Distributed Learning for MEC-Equipped Satellite 6G Networks
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DOI：
10.1109/jsac.2023.3242700
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发表时间：
2023-04
影响因子：
16.4
通讯作者：
Tiago Koketsu Rodrigues;Nei Kato
中科院分区：
文献类型：
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作者：
Tiago Koketsu Rodrigues;Nei Kato
对于未来的6G网络，重要的是保持无处不在的连接，将处理繁重的应用程序带到偏远地区，并分析海量数据以高效地提供服务。为了实现这些目标，文献利用卫星网络到达远离网络核心的地区，甚至还研究了为这些卫星配备边缘云服务器，以向远程设备提供计算分流。然而，分析这些设备创造的大数据仍然是一个问题。人们可以将数据传输到中央服务器，但这样做的传输成本很高。人们可以通过分布式机器学习来处理数据，但这种技术不如集中式学习效率高。因此，在本文中，我们分析了集中式和分布式学习背后的学习成本，并提出了一种在配备云服务器的卫星网络中自适应地利用两者的优势的混合解决方案。我们的建议可以根据当前场景确定每个设备的最佳学习策略。结果表明，该方案不仅能有效地解决机器学习问题，而且能在保持较高性能的同时动态地对不同的配置做出反应。
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.