Federated Learning Over Energy Harvesting Wireless Networks

Rami Hamdi, Mingzhe Chen, Ahmed Ben Said, Marwa Qaraqe, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review


In this article, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base stations (BSs) employs massive multiple-input–multiple-output (MIMO) to serve a set of users powered by independent energy harvesting sources. Since a certain number of users may not be able to participate in FL due to interference and energy constraints, a joint energy management and user scheduling problem in FL over wireless systems is formulated. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To find how the transmit power, the number of scheduled users and user association, affect the training loss, the FL convergence rate is first analyzed. Given this analytical result, the original optimization problem can be decomposed, simplified, and solved. Simulation results show that the proposed user scheduling and user association algorithm can reduce training loss compared to a standard FL algorithm.
Original languageEnglish
Pages (from-to)92-103
Number of pages11
JournalIEEE Internet of Things Journal
Issue number1
Early online date14 Jun 2021
Publication statusPublished - 1 Jan 2022


  • Energy harvesting
  • Federated Learning (FL)
  • Resoource Allocation
  • Wireless Networks
  • Data Models
  • Convergence


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