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A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed

Author

Listed:
  • Tarek Berghout

    (Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria)

  • Toufik Bentrcia

    (Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria)

  • Mohamed Amine Ferrag

    (Department of Computer Science, University of Guelma, Guelma 24000, Algeria)

  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge devices of smart infrastructures to train a collaborative model independently while keeping data localized. FL algorithms, encompassing a well-structured average of the training parameters (e.g., the weights and biases resulting from training-based stochastic gradient descent variants), are subject to many challenges, namely expensive communication, systems heterogeneity, statistical heterogeneity, and privacy concerns. In this context, our paper targets the four aforementioned challenges while focusing on reducing communication and computational costs by involving recursive least squares (RLS) training rules. Accordingly, to the best of our knowledge, this is the first time that the RLS algorithm is modified to completely accommodate non-independent and identically distributed data (non-IID) for federated transfer learning (FTL). Furthermore, this paper also introduces a newly generated dataset capable of emulating such real conditions and of making data investigation available on ordinary commercial computers with quad-core microprocessors and less need for higher computing hardware. Applications of FTL-RLS on the generated data under different levels of complexity closely related to different levels of cardinality lead to a variety of conclusions supporting its performance for future uses.

Suggested Citation

  • Tarek Berghout & Toufik Bentrcia & Mohamed Amine Ferrag & Mohamed Benbouzid, 2022. "A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed," Mathematics, MDPI, vol. 10(19), pages 1-16, September.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3528-:d:927530
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    References listed on IDEAS

    as
    1. Berghout, Tarek & Benbouzid, Mohamed & Muyeen, S.M., 2022. "Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    2. Berghout, Tarek & Benbouzid, Mohamed, 2022. "EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    Full references (including those not matched with items on IDEAS)

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