  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.
