Particle Swarm Optimized Federated Learning for Industrial IoT and Smart City Services

Basheer Qolomany, Kashif Ahmad, Ala Al-Fuqaha, Junaid Qadir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations


Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices. One of the main challenges for successful and efficient training and inference on edge devices is the careful selection of parameters to build local Machine Learning (ML) models. To this aim, we propose a Particle Swarm optimization (PSO)-based technique to optimize the hyperparameter settings for the local ML models in an FL environment. We evaluate the performance of our proposed technique using two case studies. First, we consider smart city services, and use an experimental transportation dataset for traffic prediction as a proxy for this setting. Second, we consider Industrial IoT(IIoT) services, and use the real-time telemetry dataset to predict the probability that a machine will fail shortly due to component failures. Our experiments indicate that PSO provides an efficient approach for tuning the hyperparameters of deep Long short-term memory (LSTM) models when compared to the grid search method. Our experiments illustrate that the number of client-server communication rounds to explore the landscape of configurations to find the near-optimal parameters are greatly reduced (roughly by two orders of magnitude needing only 2%-4% of the rounds compared to state of the art non-PSO-based approaches). We also demonstrate that utilizing the proposed PSO-based technique to find the near-optimal configurations for FL and centralized learning models does not adversely affect the accuracy of the models.

Original languageEnglish (US)
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728182988
StatePublished - Dec 2020
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: Dec 7 2020Dec 11 2020

Publication series

Name2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings


Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
Country/TerritoryTaiwan, Province of China
CityVirtual, Taipei


  • Deep Learning
  • Federated Learning
  • IoT
  • Parameter optimization
  • Particle Swarm optimization
  • Smart Services

ASJC Scopus subject areas

  • Media Technology
  • Modeling and Simulation
  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Safety, Risk, Reliability and Quality


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