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Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Open accessAbstract
Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noise. Then we develop a theoretical convergence bound on the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) there is a tradeoff between convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) given a fixed privacy protection level, increasing the number$N$of overall clients participating in FL can improve the convergence performance; and 3) there is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a$K$-client random scheduling strategy, where$K$($1\leq K< N$) clients are randomly selected from the$N$overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the$K$-client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal$K$that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the design of various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.
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In the citing paper
“Differentially private training algorithms62–64 allow for mitigation of information leakage from both the collaborator model updates and the global consensus aggregated models.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [63]
In the citing paper
“When it comes to FL, to prevent information leakage the concept of DPis utilized by adding noise to the parameters that are exchanged by the participants before model aggregation at the server side [25].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [25]
In the citing paper
“Three ML models are employed, including perceptron, support vector machine (SVM), and logistic regression, to perform FL training, showing that a competitive accuracy performance is achieved by using differential privacy-based FL methods compared to traditional FL schemes.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [82]
In the citing paper
“This can be done by using perturbation techniques [25] such as differential privacy can be used to protect training datasets against data breach, by constructing composition theorems with complex mathematical solutions.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [25]
In the citing paper
“Although some techniques have been used to overcome this problem, including differential privacy [166], these approaches degrade model performance or require additional conditions that are not suitable for IoT networks, especially high computing power [167].”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [166]
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Mishra, K. N., Bhattacharjee, V., Saket, S., & Mishra, S. P. (2022) · Cluster Computing · doi:10.1007/s10586-022-03813-x · Cited by 25
A Game-Theoretic Federated Learning Framework for Data Quality Improvement
Zhang, L., Zhu, T., Xiong, P., Zhou, W., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3230959 · Cited by 25
Optimizing the Numbers of Queries and Replies in Convex Federated Learning With Differential Privacy
Zhou, Y., Liu, X., Fu, Y., Wu, D., Wang, J. H., & Yu, S. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3234599 · Cited by 29
FedBranched: Leveraging Federated Learning for Anomaly-Aware Load Forecasting in Energy Networks
Manzoor, H. U., Khan, A. R., Flynn, D., Alam, M. M., Akram, M., Imran, M. A., & Zoha, A. (2023) · Sensors · doi:10.3390/s23073570 · Cited by 24
Heavy Hitter Identification Over Large-Domain Set-Valued Data With Local Differential Privacy
Zhu, Y., Cao, Y., Xue, Q., Wu, Q., & Zhang, Y. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3324726 · Cited by 25
Distributed Deep Learning in Open CollaborationsPreprint
Diskin, M., Bukhtiyarov, A., Ryabinin, M., Saulnier, L., Lhoest, Q., Sinitsin, A., Popov, D., Pyrkin, D., Kashirin, M., Borzunov, A., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10207 · Cited by 31
Federated learning as a smart tool for research on infectious diseases
Zwiers, L. C., Grobbee, D. E., Uijl, A., & Ong, D. S. Y. (2024) · BMC Infectious Diseases · doi:10.1186/s12879-024-10230-5 · Cited by 24
“Differential privacy methods have been introduced to overcome this issue by adding artificial noise to outputs [57], but these methods come at the cost of decreased accuracy.”
Secure Intrusion Detection by Differentially Private Federated Learning for Inter-Vehicle Networks
Xu, Q., Zhang, L., Ou, D., & Yu, W. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231159118 · Cited by 24
Cloud-Edge Collaborative Large Model Services: Challenges and Solutions
Pan, Y., Su, Z., Wang, Y., Guo, S., Liu, H., Li, R., & Wu, Y. (2024) · IEEE Network · doi:10.1109/mnet.2024.3442880 · Cited by 24
Landscape of machine learning evolution: privacy-preserving federated learning frameworks and tools
Nguyen, G., Díaz, J. S.-P., Calatrava, A., Berberi, L., Lytvyn, O., Kozlov, V. Y., Tran, V., Moltó, G., & García, Á. L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11036-2 · Cited by 32
Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation
Arora, S. S., Beams, A., Chatzigiannis, P., Meiser, S., Patel, K., Raghuraman, S., Rindal, P., Shah, H., Wang, Y., Wu, Y., et al. (2024) · Annual Computer Security Applications Conference Workshops (ACSAC Workshops) · doi:10.1109/acsacw65225.2024.00038 · Cited by 30
Self-citationFederated reinforcement learning for sustainable and cost-efficient energy management
Sievers, J., Henrich, P., Beichter, M., Mikut, R., Hagenmeyer, V., Blank, T., & Simon, F. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100521 · Cited by 25
Metadata
- Authors
- Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H.
- Affiliations
- Nanjing University of Science and Technology (CN)
- Tomsk Polytechnic University (RU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Singapore University of Technology and Design (SG)
- Southeast University (CN)
- Department of Electrical Engineering, Princeton University, Princeton, NJ
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2020
- Volume
- 15
- Pages
- 3454-3469
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W3016632787
- Cited by
- 2,400
- References
- 40
- Citations by year
- 2026: 382 · 2025: 555 · 2024: 577 · 2023: 399 · 2022: 284 · 2021: 162 · 2020: 41
- FWCI
- 87.08
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2018YFB1004800)
- National Natural Science Foundation of China (61872184, 61727802)
- SUTD Growth Plan Grant for AI
- U.S. National Science Foundation (CCF-1908308)
- Princeton Center for Statistics and Machine Learning under a Data X Grant
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- federated learning, differential privacy, artificial noise, convergence bounds, privacy-utility trade-off, communication rounds, deep neural network training, information leakage
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