Federated learning is a learning method for training models over multiple participants without directly sharing their raw data, and it has been expected to be a privacy protection method for training data. In contrast, attack methods have been studied to restore learning data from model information shared with clients, so enhanced security against attacks has become an urgent problem. Accordingly, in this article, we propose a novel framework of federated learning on the bases of the embedded structure of the vision transformer by using the model information encrypted with a random sequence. In image classification experiments, we verify the effectiveness of the proposed method on the CIFAR-10 dataset in terms of classification accuracy and robustness against attacks.

翻译：联邦学习是一种无需直接共享原始数据即可在多个参与者间训练模型的学习方法，已被视为训练数据的隐私保护手段。然而，当前已出现通过客户端共享的模型信息恢复学习数据的攻击方法，因此增强对攻击的防护能力已成为亟待解决的问题。为此，本文提出一种新型联邦学习框架，该框架基于视觉Transformer的嵌入结构，利用随机序列加密模型信息。在图像分类实验中，我们通过CIFAR-10数据集验证了该方法在分类准确性和攻击鲁棒性方面的有效性。