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.

翻译：联合会学习的重要问题之一是如何处理不平衡的数据。这一贡献引进了一种新颖技术,旨在利用I-GSM方法创造的对抗性投入处理标签偏斜的非IID数据,反向投入指导培训过程,使加权联合会能够更加重视当地标签分布的客户。 通过图像分类任务收集的用于MNIST和CIFAR-10数据集的实验结果得到报告和分析。