Budgeted online selection of candidate iot clients to participate in federated learning

Ihab Mohammed, Shadha Tabatabai, Ala Al-Fuqaha, Faissal El Bouanani, Junaid Qadir, Basheer Qolomany, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Machine learning (ML), and deep learning (DL) in particular, play a vital role in providing smart services to the industry. These techniques, however, suffer from privacy and security concerns since data are collected from clients and then stored and processed at a central location. Federated learning (FL), an architecture in which model parameters are exchanged instead of client data, has been proposed as a solution to these concerns. Nevertheless, FL trains a global model by communicating with clients over communication rounds, which introduces more traffic on the network and increases the convergence time to the target accuracy. In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process. Next, we propose an online stateful FL heuristic to find the best candidate clients. Additionally, we propose an IoT client alarm application that utilizes the proposed heuristic in training a stateful FL global model based on IoT device-type classification to alert clients about unauthorized IoT devices in their environment. To test the efficiency of the proposed online heuristic, we conduct several experiments using a real data set and compare the results against state-of-the-art algorithms. Our results indicate that the proposed heuristic outperforms the online random algorithm with up to 27% gain in accuracy. Additionally, the performance of the proposed online heuristic is comparable to the performance of the best offline algorithm.

Original languageEnglish (US)
Article number9249424
Pages (from-to)5938-5952
Number of pages15
JournalIEEE Internet of Things Journal
Issue number7
StatePublished - Apr 1 2021


  • Classification
  • Internet of Things (IoT)
  • deep learning (DL)
  • federated learning (FL)
  • machine learning (ML)
  • online algorithms
  • secretary problem

ASJC Scopus subject areas

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


Dive into the research topics of 'Budgeted online selection of candidate iot clients to participate in federated learning'. Together they form a unique fingerprint.

Cite this