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Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems

arXiv 2103.03444 ↗First posted 5 Mar 2021eess.SP cs.LGcited by 0

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Abstract (arXiv)

Limited radio frequency (RF) resources restrict the number of users that can participate in federated learning (FL) thus affecting FL convergence speed and performance. In this paper, we first introduce visible light communication (VLC) as a supplement to RF in FL and build a hybrid VLC/RF communication system, in which each indoor user can use both VLC and RF to transmit its FL model parameters. Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance. The problem is first separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The final user selection and bandwidth allocation are obtained by iteratively solving these two subproblems. Simulation results show that the proposed FL algorithm that efficiently uses VLC and RF for FL model transmission can improve the prediction accuracy by up to 10% compared with a conventional FL system using only RF.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 5 Mar 2021.

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10 of 15 references.

References · 10

  1. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  2. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  3. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  4. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  5. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  6. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  7. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  8. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  9. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  10. 2015Distributed Optimization with Arbitrary Local Solverscited by 23

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