论文标题
使用联邦学习在智能城市中利用未标记的数据
Exploiting Unlabeled Data in Smart Cities using Federated Learning
论文作者
论文摘要
隐私问题被认为是智能城市中的主要挑战之一，因为共享敏感的数据会给人们带来威胁性问题。联邦学习已成为一种有效的技术，可避免侵犯隐私并增加数据的利用。但是，在智能城市中收集的标记数据量和大量未标记数据存在稀缺性，因此需要使用半监督的学习。我们提出了一种名为FEDSEM的半监督联合学习方法，该方法利用未标记的数据。该算法分为两个阶段，第一阶段基于标记的数据训练全局模型。在第二阶段，我们使用基于伪标签技术的半监督学习来改善模型。我们使用流量标志数据集进行了几项实验，以表明FEDSEM可以通过在学习过程中利用未标记的数据来提高高达8％的准确性。
Privacy concerns are considered one of the main challenges in smart cities as sharing sensitive data brings threatening problems to people's lives. Federated learning has emerged as an effective technique to avoid privacy infringement as well as increase the utilization of the data. However, there is a scarcity in the amount of labeled data and an abundance of unlabeled data collected in smart cities, hence there is a need to use semi-supervised learning. We propose a semi-supervised federated learning method called FedSem that exploits unlabeled data. The algorithm is divided into two phases where the first phase trains a global model based on the labeled data. In the second phase, we use semi-supervised learning based on the pseudo labeling technique to improve the model. We conducted several experiments using traffic signs dataset to show that FedSem can improve accuracy up to 8% by utilizing the unlabeled data in the learning process.
