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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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Gürsoy, G., Chielle, E., Brannon, C. M., Maniatakos, M., & Gerstein, M. (2021) · Cell Systems · doi:10.1016/j.cels.2021.10.003 · Cited by 46
A Communication-Efficient Federated Learning Scheme for IoT-Based Traffic Forecasting
Zhang, C., Cui, L., Yu, S., & Yu, J. J. Q. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3132363 · Cited by 49
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
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Distributed Learning for Wireless Communications: Methods, Applications and Challenges
Qian, L., Yang, P., Xiao, M., Dobre, O. A., Renzo, M. D., Li, J., Han, Z., Qin, Y., & Zhao, J. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3156756 · Cited by 47
Self-citationPrivacy Preservation for Federated Learning With Robust Aggregation in Edge Computing
Liu, W., Xu, X., Li, D., Qi, L., Dai, F., Dou, W., & Ni, Q. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3229122 · Cited by 46
FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity
Gong, Y., Li, Y., & Freris, N. M. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00238 · Cited by 52
Federated Learning With Nesterov Accelerated Gradient
Yang, Z., Bao, W., Yuan, D., Tran, N. H. K., & Zomaya, A. Y. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3206480 · Cited by 53
Zhang, Y., Suleiman, B., Alibasa, M. J., & Farid, F. (2024) · Journal of Network and Systems Management · doi:10.1007/s10922-023-09782-9 · Cited by 51
Lightweight Federated Learning for Large-Scale IoT Devices With Privacy Guarantee
Wei, Z., Pei, Q., Zhang, N., Liu, X., Wu, C., & Taherkordi, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3127886 · Cited by 44
Federated learning to comply with data protection regulations
Chalamala, S. R., Kummari, N. K., Singh, A. K., Saibewar, A., & Chalavadi, K. M. (2022) · CSI Transactions on ICT · doi:10.1007/s40012-022-00351-0 · Cited by 45
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Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge
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Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning
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Over-the-air federated learning: Status quo, open challenges, and future directions
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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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