Joint Scheduling and Resource Allocation for Efficiency-Oriented Distributed Learning Over Vehicle Platooning Networks

• Published in: IEEE transactions on vehicular technology Vol. 70; no. 10; pp. 10894 - 10908
• Main Authors: Ma, Xiaoting, Zhao, Junhui, Gong, Yi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Computational modeling; Computer aided instruction; Convergence; Distance learning; Distributed databases; distributed learning; Efficiency; Heterogeneity; Learning; Markovian stochastic process; Networks; Optimization; Platooning; Processor scheduling; Resource allocation; Resource scheduling; Scheduling; Stochastic processes; vehicle platooning networks; Vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2021.3107465

The limited communication and computing resources, as well as the rising concerns about the privacy protection, bring significant challenges to the massive data training and analysis in vehicular networks. To address these challenges, in this paper a platoon-based distributed learning framework design for data learning is carried out, where the vacant computation resources of vehicle platooning networks are leveraged. In the proposed framework, a 2-phase Markovian stochastic process is utilized to depict the learning service heterogeneity for each participating vehicle. Meanwhile, we propose a joint scheduling and resource allocation scheme for efficiency-oriented distributed learning to maximize the learning accuracy subject to a given learning time constraint. The optimization problem is solved as follows. First, given the scheduled vehicles, the communication resource allocation is modeled as a minimum-maximum problem to minimize the learning delay of each learning round. Subsequently, an efficiency-oriented unbiased global aggregation policy is proposed to explore the convergence difference between partial scheduling and total scheduling. Considering the learning convergence and remaining time, an on-demand scheduling scheme is introduced to determine the number of scheduled vehicles. Finally, combining the learning efficiency of each vehicle with the scheduled number of vehicles, the scheduled vehicle set is selected. Simulations results show that the proposed scheduling policy can schedule the number of participating vehicles on demand based on the trade-off between learning performance and learning latency.