Research Article
FSVM: Federated Support Vector Machines for Smart City
@INPROCEEDINGS{10.1007/978-3-031-33458-0_11, author={Lichuan Ma and Lizhen Tang and Longxiang Gao and Qingqi Pei and Ming Ding}, title={FSVM: Federated Support Vector Machines for Smart City}, proceedings={Tools for Design, Implementation and Verification of Emerging Information Technologies. 17th EAI International Conference, TridentCom 2022, Melbourne, Australia, November 23-25, 2022, Proceedings}, proceedings_a={TRIDENTCOM}, year={2023}, month={6}, keywords={Federated Support Vector Machines Privacy Preserving ADMM}, doi={10.1007/978-3-031-33458-0_11} }- Lichuan Ma
Lizhen Tang
Longxiang Gao
Qingqi Pei
Ming Ding
Year: 2023
FSVM: Federated Support Vector Machines for Smart City
TRIDENTCOM
Springer
DOI: 10.1007/978-3-031-33458-0_11
Abstract
By putting digital technology and vast volume of data together, smart city becomes an emerging city paradigm for intelligent city management and operation. As one of the most popular artificial intelligent algorithms, support vector machines (SVMs) have been widely adopted for classification in various smart city applications. Due to the explosion of data and rigorous privacy requirements, an SVM classifier needs to be trained in a distributed and privacy-preserving manner. To achieve this, a federated SVM (FSVM) scheme is proposed to collaboratively and privately train an SVM classifier by combining the alternating direction method of multipliers (ADMM) with secret sharing. Specifically, the FSVM consists of FSVM-C and FSVM-S to deal with two cases of data partitioning by examples and features, respectively. By implementing the FSVM scheme on the real-word dataset MNIST, the efficiency and effectiveness of both FSVM-S and FSVM-C are verified by comprehensive experimental results.
