Semi-Supervised Decentralized Machine Learning with Device-to-Device Cooperation

The massive data from mobile and embedded devices have huge potential for training machine learning models. Decentralized machine learning (DML) can avoid the inherent bottleneck of the parameter server (PS) by collaboratively training models in a device-to-device (D2D) fashion. However, the previous DML works often assume that the local data are fully annotated with ground-truth labels, which is unrealistic for many Internet of Things (IoT) applications. This arises a new practical DML scenario, namely semi-supervised DML, where the local data of distributed workers are partially labeled in the D2D network. The existing semi-supervised learning techniques are proposed for standalone or the PS architecture, which ignore the impact of D2D topology on the performance of semi-supervised learning. Thus, they cannot adequately leverage the unlabeled data of decentralized workers, leading to performance degradation. Herein, we propose a novel framework, called SSD, to address the problem of semi-supervised DML by exploiting D2D cooperation. The key insight behind SSD is that neighbor selection has a crucial impact on pseudo-label quality and communication overhead. In SSD, each worker adaptively selects its neighbors with high-quality models and similar data distribution under communication resource constraints, which helps to generate high-confidence pseudo-labels for local unlabeled data and further boosts the DML performance. Extensive empirical evaluations on both testbed and simulated environments show that SSD significantly outperforms other baselines.