Toward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach

Privacy-preserving transportation mode identification (TMI) is among the key challenges toward future intelligent transportation systems. With recent developments in federated learning (FL), crowdsourcing has emerged as a promising cost-effective data source for training powerful TMI classifiers without compromising users' data privacy. However, existing TMI approaches have relied heavily on the availability of transportation mode labels, which is often limited in real-world applications. While recent semisupervised studies have partially addressed this issue by assigning pseudolabels to unlabeled data, such practice often degrades classification performance as more unlabeled data are incorporated. In response to this issue, we present a semisupervised FL scheme for TMI termed mean teacher semisupervised FL (MTSSFL). MTSSFL trains a deep neural network ensemble under a novel semisupervised FL framework, achieving highly accurate and privacy-protected crowdsourced TMI without depending on the availability of massive labeled data. MTSSFL introduces consistency updating to insert the global model in the gradient updates of the local models that only have unlabeled data to improve their training. We also devise mean-teacher-averaging, a secure parameter aggregation mechanism that further boosts the global model's TMI performance without requiring additional training. Our extensive case studies on a real-world data set demonstrate that MTSSFL's classification accuracy is merely 1.1% lower than the state-of-the-art semisupervised TMI approach while being the only one to satisfy FL's privacy-preserving constraints. In addition, MTSSFL can achieve high accuracy with less training overhead due to the proposed semisupervised learning design.

Automated summary

The study proposes a novel semisupervised FL scheme named mean teacher semisupervised FL (MTSSFL) for privacy-protected transportation mode identification. By inserting the global model in the gradient updates of the local models that only have unlabeled data, MTSSFL achieves highly accurate and privacy-protected TMI. Additionally, mean-teacher-averaging is employed as a secure parameter aggregation mechanism to further enhance the global model's performance without extra training.