Federated Learning for Task and Resource Allocation in Wireless High-Altitude Balloon Networks

Sihua Wang, Mingzhe Chen, Changchuan Yin, Walid Saad, Choong Seon Hong, Shuguang Cui, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In this article, the problem of minimizing energy and time consumption for task computation and transmission in mobile-edge computing-enabled balloon networks is investigated. In the considered network, high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational capabilities to process the computational tasks offloaded from their associated users. Since the data size of each user's computational task varies over time, the HABs must dynamically adjust their resource allocation schemes to meet the users' needs. This problem is posed as an optimization problem, whose goal is to minimize the energy and time consumption for task computation and transmission by adjusting the user association, service sequence, and task allocation schemes. To solve this problem, a support vector machine (SVM)-based federated learning (FL) algorithm is proposed to determine the user association proactively. The proposed SVM-based FL method enables HABs to cooperatively build an SVM model that can determine all user associations without any transmissions of either user historical associations or computational tasks to other HABs. Given the predictions of the optimal user association, the service sequence and task allocation of each user can be optimized so as to minimize the weighted sum of the energy and time consumption. Simulations with real-city cellular traffic data show that the proposed algorithm can reduce the weighted sum of the energy and time consumption of all users by up to 15.4% compared to a conventional centralized method.

Original languageEnglish (US)
Pages (from-to)17460-17475
Number of pages16
JournalIEEE Internet of Things Journal
Volume8
Issue number24
DOIs
StatePublished - Dec 15 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Federated learning (FL)
  • Support vector machine (SVM)
  • Task offloading
  • User association

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