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Privacy-Preserving Federated Machine Learning Techniques

Privacy-Preserving Federated Machine Learning Techniques

Gobinath Subramaniam, Santhiya Palanisamy
Copyright: © 2023 |Pages: 24
ISBN13: 9798369305935|ISBN13 Softcover: 9798369348529|EISBN13: 9798369305942
DOI: 10.4018/979-8-3693-0593-5.ch007
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MLA

Subramaniam, Gobinath, and Santhiya Palanisamy. "Privacy-Preserving Federated Machine Learning Techniques." Privacy Preservation and Secured Data Storage in Cloud Computing, edited by Lakshmi D. and Amit Kumar Tyagi, IGI Global, 2023, pp. 154-177. https://doi.org/10.4018/979-8-3693-0593-5.ch007

APA

Subramaniam, G. & Palanisamy, S. (2023). Privacy-Preserving Federated Machine Learning Techniques. In L. D. & A. Tyagi (Eds.), Privacy Preservation and Secured Data Storage in Cloud Computing (pp. 154-177). IGI Global. https://doi.org/10.4018/979-8-3693-0593-5.ch007

Chicago

Subramaniam, Gobinath, and Santhiya Palanisamy. "Privacy-Preserving Federated Machine Learning Techniques." In Privacy Preservation and Secured Data Storage in Cloud Computing, edited by Lakshmi D. and Amit Kumar Tyagi, 154-177. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/979-8-3693-0593-5.ch007

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Abstract

Machine learning is increasingly used for data analysis, but centralized datasets raise concerns about data privacy and security. Federated learning, a distributed method, enables multiple entities to cooperatively train a machine learning model. Clients use their local datasets to train local models, while a central aggregator aggregates updates and computes a global model. Privacy-preserving federated learning (PPFL) addresses privacy issues in sensitive and decentralized data situations. PPFL integrates federated learning with privacy-preserving approaches to achieve both privacy and model correctness.

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