Personalized Federated Learning With Differential Privacy
Abstract
To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and training machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This article proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. In addition, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. The experimental results on realistic mobile sensing data demonstrate that the proposed approach is robust to user heterogeneity and offers a good tradeoff between accuracy and privacy.
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Differential privacy is applied by inserting artificial noise (e.g., Gaussian noise) to the gradients of neural network layers to preserve training data and hidden personal information against external threats while the convergence property is guaranteed.
“As an example, differential privacy is applied in [189] by inserting artificial noise (e.g., Gaussian noise) to the gradients of neural network layers to preserve training data and hidden personal information against external threats while the convergence property is guaranteed.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [189]
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Federated Learning for Internet of Things: A Comprehensive Survey
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“As an example, differential privacy is applied in [189] by inserting artificial noise (e.”
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- Authors
- Hu, R., Guo, Y., Li, H., Pei, Q., & Gong, Y.
- Affiliations
- The University of Texas at San Antonio (US)
- Xidian University (CN)
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-10
- Volume
- 7
- Issue
- 10
- Pages
- 9530-9539
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3023244064
- Cited by
- 350
- References
- 2
- Citations by year
- 2026: 42 · 2025: 68 · 2024: 81 · 2023: 85 · 2022: 45 · 2021: 25 · 2020: 4
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- U.S. National Science Foundation (CNS-1850623, CNS-2029685)
- Key Program of NSFC-Tongyong Union Foundation (U1636209)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- personalized federated learning, differential privacy, privacy-preserving machine learning, user heterogeneity, convergence analysis, smart devices
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