作者：
Huo, XinZhang, HaoWang, ZhupingYan, HuaichengLiu, ChunTongji Univ
Dept Control Sci & Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 200092 Peoples R China Tongji Univ
Frontiers Sci Ctr Intelligent Autonomous Syst Minist Educ Shanghai 200092 Peoples R China East China Univ Sci & Technol
Sch Informat Sci & Engn Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Tongji Univ
Coll Surveying & Geoinformat Shanghai 200092 Peoples R China
distributed machine learning has emerged as a promising data processing technology for next-generation communication systems. It leverages the computational capabilities of local nodes to efficiently handle large data...
详细信息
distributed machine learning has emerged as a promising data processing technology for next-generation communication systems. It leverages the computational capabilities of local nodes to efficiently handle large datasets, creating highly accurate data-driven models for analysis and prediction purposes. However, the performance of distributed machine learning can be significantly hampered by communication bottlenecks and node dropouts. In this article, a novel unmanned aerial vehicle (UAV)-enabled hierarchical distributed learning architecture is proposed to support machine learning applications, e.g., regional monitoring. Multiple UAV receivers (URs) are introduced as wireless relays to improve the communication between the UAV transmitters (UTs) and the cloud server. Our objective is to identify the optimal UT-UR association to maximize the social welfare of the network, which is distinctly different from the existing works that focus on the unilateral profit-maximizing problem. We formulate a two-side many-to-one matching game to model the UT-UR association problem, and a two-phase many-to-one matching algorithm is designed to identify the stable matching. The validity of our proposed scheme is verified through in-depth numerical simulations.
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