Exploiting Unlabeled Data in Smart Cities using Federated Learning
By Abdullatif Albaseer, Bekir Sait Ciftler, Mohamed M. Abdallah, Ala Al-Fuqaha. 2020
20 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Transportation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Qiang Yang
- Murali Annavaram
- Chaoyang He
- Yang Liu
- Sung Ju Hwang
- S. Avestimehr
- Jaehong Yoon
- Praneeth Vepakomma
In Transportation
- Peilin Zhao
- Murali Annavaram
- Ramesh Raskar
- Qiang Yang
- Chunyan Miao
- Dusist Niyato
- Praneeth Vepakomma
- Chaoyang He
Citations in the corpus
- FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], finance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106, 107], robotics [108, 109], and smart cities [110, 111].”
- Federated Semi-Supervised Learning with Inter-Client Consistency (2020, background): “Recently, interests of tackling scarcity of labeled data in FL are emerging, and the pioneering survey-level studies are discussed in [15, 16, 17].”
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “Emerging studies have also considered the use of semi-supervised learning inspired techniques [198].”