Unsupervised Federated Adversarial Domain Adaptation for Heterogeneous Internet of Things

Federated learning, as a novel machine learning paradigm, aims to collaboratively train a global model while keeping the training data on local devices, which protects data privacy and security of distributed devices. However, the model cannot generalize to new devices because of domain shift caused by the statistical difference between the labeled data and unlabeled data collected by different devices in heterogeneous internet of things networks. In this paper, we propose a method named Unsupervised Federated Adversarial Domain Adaptation with Controller Modules (UFADACM), which aims to reduce the distribution difference between source nodes with labeled data and target nodes with unlabeled data, and reduce the parameter cost and communication overhead while achieving a comparable performance. We also conduct extensive experiments to demonstrate the effectiveness of the proposed method.