基于自编码神经网络的半监督联邦学习模型

联邦学习是一种新型的分布式机器学习方法，可以使得各客户端在不分享隐私数据的前提下共同建立共享模型。然而现有的联邦学习框架仅适用于监督学习，即默认所有客户端数据均带有标签。由于现实中标记数据难以获取，联邦学习模型训练的前提假设通常很难成立。为解决此问题，对原有联邦学习进行扩展，提出了一种基于自编码神经网络的半监督联邦学习模型ANN-SSFL,该模型允许无标记的客户端参与联邦学习。无标记数据利用自编码神经网络学习得到可被分类的潜在特征，从而在联邦学习中提供无标记数据的特征信息来作出自身贡献。在MNIST数据集上进行实验，实验结果表明，提出的ANN-SSFL模型实际可行，在监督客户端数量不变的情况下，增加无监督客户端可以提高原有联邦学习精度。

Semi-supervised federated learning model based on AutoEncoder neural network

Federated learning is a novel distributed machine learning approach, which provides a privacy protection way to learn a shared model without sharing each client''s private data. However, the existing frameworks of federated learning only work for supervised learning wherein each client''s data is labeled. Since collecting labeled data is difficult and expensive to obtain in the real world, the assumption of federated learning is not valid. To solve this problem, this paper proposed a semi-supervised federated learning model named ANN-SSFL based on an autoencoder neural network. The proposed model was extended from classical federated learning and allowed clients who might not have labeled data to participate the federated learning. The latent features which could be identified by the classifier were obtained by autoencoder neural network from unlabeled data, therefore unlabeled data could provide their data information to make their contributions. This paper conducted experiments on MNIST data sets. The experimental results show that the proposed ANN-SSFL is practical and effective. When the number of supervised clients remains unchanged, adding unsupervised clients can improve the accuracy of classical federated learning.