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Smart city article details
| Title | Fsvm: Federated Support Vector Machines For Smart City |
|---|---|
| ID_Doc | 27357 |
| Authors | Ma L.; Tang L.; Gao L.; Pei Q.; Ding M. |
| Year | 2023 |
| Published | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 489 LNICST |
| DOI | http://dx.doi.org/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. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. |
| Author Keywords | ADMM; Federated Support Vector Machines; Privacy Preserving |
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