mlsanityData to 14 Oct 2026

Using adversarial images to improve outcomes of federated learning for non-IID data

arXiv 2206.08124 ↗First posted 16 Jun 2022cs.LGcited by 1

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Abstract (arXiv)

One of the important problems in federated learning is how to deal with unbalanced data. This contribution introduces a novel technique designed to deal with label skewed non-IID data, using adversarial inputs, created by the I-FGSM method. Adversarial inputs guide the training process and allow the Weighted Federated Averaging to give more importance to clients with 'selected' local label distributions. Experimental results, gathered from image classification tasks, for MNIST and CIFAR-10 datasets, are reported and analyzed.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 19 references that are in the map. First posted on arXiv 16 Jun 2022.

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16 of 19 references.

References · 16

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4 more
  1. 2021A Review of Adversarial Attack and Defense for Classification Methodscited by 29
  2. 2021Demystifying the Transferability of Adversarial Attacks in Computer Networkscited by 9
  3. 2021An Experimental Study of Class Imbalance in Federated Learningcited by 7
  4. 2021Exploring Transferable and Robust Adversarial Perturbation Generation from the Perspective of Network Hierarchycited by 4

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Citing papers per year: 2022: 1.

1 paper.

  1. 2022StatMix: Data augmentation method that relies on image statistics in federated learningcited by 4