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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Mitigating the effects of non-IID data in federated learning with a self-adversarial balancing method

DOI: http://dx.doi.org/10.15439/2023F6549

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 925930 ()

Full text

Abstract. Federated learning (FL) allows multiple devices to jointly train a global model without sharing local data. One of its problems is dealing with unbalanced data. Hence, a novel technique, designed to deal with label-skewed non-IID data, using adversarial inputs is proposed. Application of the proposed algorithm results in faster, and more stable, global model performance at the beginning of the training. It also delivers better final accuracy and decreases the discrepancy between the performance of individual classes. Experimental results, obtained for MNIST, EMNIST, and CIFAR-10 datasets, are reported and analyzed.


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