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Ahmad Madni, H.* ; Umer, R.M. ; Luca Foresti, G.*

Exploiting data diversity in multi-domain federated learning.

Mach. Learn.: Sci. Technol. 5:025041 (2024)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
Federated learning (FL) is an evolving machine learning technique that allows collaborative model training without sharing the original data among participants. In real-world scenarios, data residing at multiple clients are often heterogeneous in terms of different resolutions, magnifications, scanners, or imaging protocols, and thus challenging for global FL model convergence in collaborative training. Most of the existing FL methods consider data heterogeneity within one domain by assuming same data variation in each client site. In this paper, we consider data heterogeneity in FL with different domains of heterogeneous data by raising the problems of domain-shift, class-imbalance, and missing data. We propose a method, multi-domain FL as a solution to heterogeneous training data from multiple domains by training robust vision transformer model. We use two loss functions, one for correctly predicting class labels and other for encouraging similarity and dissimilarity over latent features, to optimize the global FL model. We perform various experiments using different convolution-based networks and non-convolutional Transformer architectures on multi-domain datasets. We evaluate the proposed approach on benchmark datasets and compare with the existing FL methods. Our results show the superiority of the proposed approach which performs better in term of robust FL global model than the exiting methods.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Class-imbalance ; Data Heterogeneity ; Domain-shift ; Federated Learning ; Multi-domain Data
ISSN (print) / ISBN 2632-2153
e-ISSN 2632-2153
Zeitschrift Machine Learning: Science and Technology
Quellenangaben Band: 5, Heft: 2, Seiten: , Artikelnummer: 025041 Supplement: ,
Verlag Institute of Physics Publishing (IOP)
Verlagsort Temple Circus, Temple Way, Bristol Bs1 6be, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Förderungen
Department Strategic Project of the University of Udine