Attribute recognition for person re-identification using federated learning at all-in-edge

Shini Girija, Thar Baker, Naveed Ahmed, Ahmed M. Khedr, Zaher Al Aghbari, Ashish Jha, Konstantin Sobolev, Salman Ahmadi Asl, Anh-Huy Phan

Research output: Contribution to journalArticlepeer-review


The advancement in person re-identification using attribute recognition is constrained by the increasingly strict data privacy standards since it necessitates the centralization of vast amounts of data containing sensitive personal data in the cloud. Cloud-based person re-identification requires the transfer of original video information to the servers, causing increased communication costs because of the need for significant bandwidth, resulting in unpredictable timing. This work presents an all-in-edge architecture for attribute-based person re-identification, which deploys training data in edge nodes that support distributed inference. Edge nodes independently learn but collaborate with specific neighboring nodes by sharing information to minimize communication and computational costs through the utilization of federated learning and transfer learning methods. Furthermore, this paper proposes a federated aggregation strategy-FedTransferLoss to obtain optimal global accuracy by using transfer learning to re-train the low-quality local models. Extensive experiments on two prominent pedestrian datasets- PETA and RAP show that FedTransferLoss achieves higher accuracy, recall and precision values compared to the traditional FedAvg algorithm.
Original languageEnglish
Article number100793
Number of pages23
JournalInternet of Things (Netherlands)
Publication statusPublished - 25 Apr 2023

Bibliographical note

Funding Information:
The work presented in this article was supported by the joint project, between the University of Sharjah and the Skolkovo Institute of Science and Technology (SKOLTECH), Artificial Intelligence for Life (AIfoL) collaborative grant .

Publisher Copyright:
© 2023 Elsevier B.V.


  • Attribute recognition
  • Edge computing
  • Federated learning
  • Person re-identification
  • Transfer learning


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